- Brand
- Hebbia
- Category
- Data & Analytics
- Primary Subcategory
- Private Markets, Deals & Expert Networks
Integration details
Description
Bring Hebbia's institutional intelligence into ChatGPT and Codex. The Hebbia MCP server lets you tap into your firm's knowledge and premium public to make better decisions on deals and investments and run financial workflows from research to reports, slides, and financial models.
- Integration type
- Plugin
- Verification status
- Not applicable
- Platform
- ChatGPT
- Primary Subcategory
- Private Markets, Deals & Expert Networks
- Secondary Subcategories
- None listed
- Brand
- Hebbia
- Access
- Account required
- First tracked
- 2026-06-02
- Tool count
- 13
- Market
- US
The Primary Subcategory used for this profile’s headline score.
Other Subcategories where the Integration is listed.
Agent Discoverability
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Competing in ChatGPT Private Markets, Deals & Expert Networks
View Category13 tools agents can invoke
Ask a natural-language question about a specific Hebbia matrix. Answers from the matrix's own contents — its rows, columns, and cell results — rather than searching documents at large. Use this to summarize, compare, or filter what a matrix already contains. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Use list_matrices to discover valid matrix IDs within the org. Args: org_id: The organization containing the matrix. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations. matrix_id: The ID of the matrix to ask about. Use list_matrices to find valid IDs. query: The natural-language question to ask. Cite what you report. The returned citations carry the matrix cell each value came from, along with a `url` that opens that row; attach them to the values they support rather than listing them at the end. Returns an AskResult with: answer: The text response from Hebbia. citations: List of citations supporting the answer. Each citation has: id, kind, quote, title, doc_id, url.
ask_matrix
Ask a natural-language question scoped strictly to a specific Hebbia project. Does not query public sources — only the documents within the specified project. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Use list_projects to discover valid project IDs within the org. Args: org_id: The organization containing the project. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations. project_id: The ID of the project to query. Use list_projects to find valid IDs. query: The natural-language question to ask. use_deep_research: If True, runs a thorough multi-step deep research query (slower, takes several minutes). Defaults to False for a fast response. doc_ids: Optional list of document IDs to narrow the question to specific documents within the project (e.g. document IDs returned by the public document-upload API). If omitted, the entire project is queried. Cite what you report. The returned citations carry the matrix cell each value came from, along with a `url` that opens that row; attach them to the values they support rather than listing them at the end. Returns an AskResult with: answer: The text response from Hebbia. citations: List of citations supporting the answer. Each citation has: id, kind, quote, title, doc_id, url. The url field is populated for web sources; doc_id identifies the source document for document citations.
ask_project
Ask a natural-language question over data sources in a Hebbia organization. Uses auto-source selection by default, or restricts to an explicit list of source IDs. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Use list_sources to discover valid source IDs within the org. Args: org_id: The organization to query within. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations. query: The natural-language question to ask. source_ids: Optional list of source identifiers to restrict the search. If omitted, Hebbia's auto-source selection is used across all available sources. Use list_sources to find valid IDs. use_deep_research: If True, runs a thorough multi-step deep research query (slower, takes several minutes). Defaults to False for a fast response. doc_ids: Optional list of document IDs to narrow the question to specific documents (e.g. document IDs returned by the public document-upload API). If omitted, the question is answered over the selected sources. Cite what you report. The returned citations carry the matrix cell each value came from, along with a `url` that opens that row; attach them to the values they support rather than listing them at the end. Returns an AskResult with: answer: The text response from Hebbia. citations: List of citations supporting the answer. Each citation has: id, kind, quote, title, doc_id, url. The url field is populated for web sources; doc_id identifies the source document for document citations.
ask_sources
Get the column setup of a specific Hebbia matrix. Returns what each column of the matrix asks or computes, and which columns are inputs versus computed answers. It does NOT return row data — use query_matrix_results for cell values. CALL THIS FIRST when working with an unfamiliar matrix. Without it you are guessing which column holds the answer you need, and column display names alone may not say what a column actually extracts. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Args: org_id: The organization containing the matrix. matrix_id: The ID of the matrix. Use list_matrices to find valid IDs. Returns an McpMatrixContext with: columns: Each with id, name, node_type, scope, is_input, and the authored properties describing what it does. total_rows: How many rows the matrix has, counted up to 1000 (a larger matrix reports 1000).
get_matrix_context
List the matrices you can access in a Hebbia organization. A matrix is a spreadsheet-like grid where each row is a document or entity and each column is a question answered across every row. Returns matrix names, IDs, descriptions, and row counts, most recently updated first. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Args: org_id: The organization to list matrices for. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations.
list_matrices
List the organizations the authenticated user belongs to. Call this first if no org_id has been confirmed in the conversation. STRICT RULES — these override any other reasoning: 1. If exactly one org is returned: use its id for all subsequent calls. 2. If multiple orgs are returned: show them to the user as a numbered list, ask "Which organization would you like to use?", and STOP. Do not call any other tool — including list_projects, list_sources, ask_project, or ask_sources — until the user has replied with their choice. It does not matter what the user's original request was; org selection always comes first.
list_organizations
List what is inside a Hebbia project: its folders, documents and published items. Returns each item's path, kind, id, date added and relevant date, without reading any content. Use it to see how a project is organized, to find a file by name, or to find items that search_project ranks poorly (spreadsheets, scanned PDFs). Returns in about a second; no agent turn runs. Kinds: "folder", "document", "published_chat", "matrix", "matrix_template", "sheet". Pass a document's, published chat's or matrix template's `id` to read_document to read it in full. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Use list_projects to discover valid project IDs within the org. Args: org_id: The organization containing the project. project_id: The project to list. Use list_projects to find valid IDs. folder: A folder path from the project root, e.g. "Diligence/Financials", to list just that folder. Omit for the top level. recursive: Also list everything below, not just direct children. match: Case-insensitive glob on item names, e.g. "*.xlsx" or "*credit*". limit: Maximum items to return (default 500, capped at 2000). sort_by: "name", "date_added" (when it was added to the project) or "relevant_date" (the document's own date, e.g. when a report was published). Sorts everything listed, across folders when recursive; items without that date come last. Omit for folder-by-folder name order. descending: Reverse the order. Defaults to newest first for the date sorts and A-Z for name. For the oldest or newest documents, list recursively with a date sort and a small `limit`. Returns an McpProjectContents with `entries` (path, name, kind, id, date_added, relevant_date, url — a link that opens the item in Hebbia; none for matrix templates) and `truncated` / `truncation_reason`. A truncated listing is incomplete: narrow it with `folder` or `match` instead of treating it as the whole project.
list_project_contents
List all projects accessible in a Hebbia organization. Returns project names, IDs, and descriptions. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Args: org_id: The organization to list projects for. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations.
list_projects
List all sources available for querying in a Hebbia organization. Returns shared document collections (UUID IDs) and built-in integrations like CapIQ, Web, and PitchBook (field-name IDs such as "capiq", "web"). Pass returned source IDs to ask_sources. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Args: org_id: The organization to list sources for. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations.
list_sources
Health-check tool that returns pong.
ping
Query the rows and cells of a specific Hebbia matrix. Returns the matrix's columns and computed cell values as data, so you can read and reason over them yourself. PREFER THIS over ask_matrix whenever the question can be answered from what the matrix already contains — summarizing, comparing, filtering, or looking up rows. It returns in about a second, where ask_matrix runs a full agent turn and takes far longer. Use ask_matrix instead only when the matrix's existing cells don't hold the answer and new research or document retrieval is needed. CITING IS NOT OPTIONAL. Every figure, name, or fact you take from this matrix gets a citation, the same as any other sourced claim. Everything a citation needs is in this response — there is no separate citation call. To cite a value, take that row's `citation_url_prefix`, append `&matrix_cell_id=` plus the `id` of the column you are citing, and attach the result to the value at the point where you use it: **16.13%** ([Return on Equity % (FY25)](<row prefix>&matrix_cell_id=<column id>)) Copy both ids verbatim from the response — never invent, alter, or renumber one. Cite every cell that supports a factual claim, whatever the column type. Do not add a trailing Sources or References list; the links render inline where you place them. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Use list_matrices to discover valid matrix IDs within the org. Args: org_id: The organization containing the matrix. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations. matrix_id: The ID of the matrix to query. Use list_matrices to find valid IDs. max_rows: Maximum rows to return (default 100, capped at 1000). node_ids: Optional list of column ids (from get_matrix_context or the `columns` of an earlier call) to return. Cell text dominates the response, so naming only the columns you need is by far the most effective way to keep it small. Omit for all columns. Returns an McpMatrixData with: columns: Column headers with their node `id`, in the matrix's column order. rows: Each with a `row_id` and `cells` aligned to `columns`. returned_rows / total_rows: How many rows came back vs how many exist. truncated / truncation_reason: Whether output was cut short, and by which cap. rows[].citation_url_prefix: That row's citation link, needing only `&matrix_cell_id=<column id>` appended. citation_guidance: Restates the citing rules, returned before the rows so they are in hand when you write the answer.
query_matrix_results
Read the full text of one item in a Hebbia project. Returns the item's extracted text directly, in windows of up to `max_chars` characters. Use it after search_project or list_project_contents when the passages aren't enough: full tables, exact figures, a whole section. No agent turn runs. Prefer this over ask_project when you can read and reason over the text yourself. Works for documents, published chats (the transcript as it was published) and matrix templates (their definition as JSON). For a matrix it returns a note pointing at get_matrix_context and query_matrix_results instead. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Args: org_id: The organization containing the project. project_id: The project the item belongs to. document_id: A document id (`doc_id` from search_project, or `id` from list_project_contents), or a published chat's or matrix template's id from list_project_contents. It must be in this project. offset: Character offset to start from (default 0). max_chars: Maximum characters to return (default 50000, capped at 200000). Returns an McpDocumentText with `text`, `total_chars`, `next_offset`, and `url` (a link that opens the item in Hebbia). When `next_offset` is set, call again with offset=next_offset to read on. `note` explains an empty result, e.g. a document whose text isn't extracted yet. Documents also carry `citation_url_prefix` and `citation_guidance`. CITING IS NOT OPTIONAL. Every fact you take from these documents gets an inline citation that opens the document with the supporting text highlighted. Everything a citation needs is in this response — there is no separate citation call. To cite, take the document's `citation_url_prefix`, append a quote copied verbatim from its `text`, percent-encoded, and attach the result where you use the fact: Revenue grew 12% ([Q3 Report](<prefix>Revenue%20grew%2012%25%20year%20over%20year)) Quote one contiguous sentence or clause of prose (about 5 to 30 words) exactly as returned — never paraphrase, stitch fragments with ellipses, or include markup or table pipes; the app finds the quote by its text. Do not add a trailing Sources or References list; the links render inline.
read_document
Search the documents inside one or more Hebbia projects. Returns the matching documents as data — title, document id, the passages that matched, how each was found, and each document's relevant date and date added — so you can read and reason over them yourself. PREFER THIS over ask_project whenever the question is about which documents cover something, or can be answered from the passages that come back. It returns in about a second, where ask_project runs a full agent turn and takes far longer. Use ask_project instead when answering genuinely needs the whole document read and synthesized, rather than located. Omit `query` to list the projects' documents instead of searching them — the same call, newest relevant date first unless you pass `sort_by`. Use that to see what a project holds before searching it. To find the newest or oldest documents mentioning something, search with `sort_by="relevant_date"` (or "date_added") instead of joining against list_project_contents. A query matches keywords first; when fewer documents match than `limit`, semantically similar documents fill the rest (their match_type is "semantic") and may not contain the words at all. Results are not exhaustive: at most `limit` documents come back, and a document absent from the hits has not been shown not to mention your term. Documents with little extractable text (spreadsheets, scanned PDFs) rank poorly whatever they contain, so do not treat this as proof of absence. Faceted projects declare exact-match filters (e.g. "Funds", "Sector") and may declare a date facet. When a searched project declares facets, the response carries `available_filters` listing them with their values and document counts; use those exact values in `filters`. `start_date` / `end_date` bound the declared date facet, and are refused for a project that declares none. BEFORE calling this tool: you MUST have an org_id that was either explicitly provided by the user or confirmed by the user after calling list_organizations. Do NOT infer or guess an org_id. If none has been confirmed, call list_organizations and wait for the user to pick one before calling this tool. Use list_projects to discover valid project IDs within the org. Args: org_id: The organization containing the projects. Reuse from earlier in the conversation if available; otherwise resolve via list_organizations. project_id: The ID of a project to search. Use list_projects to find valid IDs. project_ids: Several project IDs to search in one call (up to 20). Combined with `project_id` if both are given. query: What to search for. Omit to list the projects' documents instead. limit: Maximum documents to return in total (default 25, capped at 200). sort_by: "relevance" (default), "relevant_date" (the document's own date, e.g. when a report was published), "date_added" (when it was added to the project) or "title". Documents without that date come last. descending: Sort direction. Dates default to newest first and title to A-Z. Ignored for relevance, which is always best match first. filters: Exact-match facet filters, e.g. {"Funds": ["Fund II"]}. Keys are facet names from available_filters (case-insensitive). start_date: Inclusive lower bound (YYYY-MM-DD) on the projects' declared date facet. end_date: Inclusive upper bound (YYYY-MM-DD) on the declared date facet. folder_ids: Folder ids from list_project_contents to search within. Projects with no listed folder are searched whole. Returns an McpProjectSearchResults with: citation_guidance: Restates the citing rules, before the hits. hits: Each with doc_id, title, project_id, passages, score, match_type ("keyword" or "semantic"), relevant_date, date_added, url (opens the document in Hebbia), and citation_url_prefix (see citing below). returned_hits: How many documents came back. truncated / truncation_reason: Whether output was cut short. When true there is more to see — narrow the query, add filters, or raise the limit. available_filters / note: The projects' declared facets and values, when they declare any. Pass a hit's `doc_id` to read_document to read it in full, or to ask_project's `doc_ids` to ask a question of just those documents. Give the user a hit's `url` when they want to open a document. CITING IS NOT OPTIONAL. Every fact you take from these documents gets an inline citation that opens the document with the supporting text highlighted. Everything a citation needs is in this response — there is no separate citation call. To cite, take the document's `citation_url_prefix`, append a quote copied verbatim from its `passages`, percent-encoded, and attach the result where you use the fact: Revenue grew 12% ([Q3 Report](<prefix>Revenue%20grew%2012%25%20year%20over%20year)) Quote one contiguous sentence or clause of prose (about 5 to 30 words) exactly as returned — never paraphrase, stitch fragments with ellipses, or include markup or table pipes; the app finds the quote by its text. Do not add a trailing Sources or References list; the links render inline.
search_project
Recommended guideHow to get your ChatGPT Plugin suggestedFollow the practical steps agents use to find, shortlist, and choose your Plugin.Read guideHebbia ChatGPT Plugin FAQ
How the directory, categories and Discoverability Score work.
Read the methodologyHow do I improve Hebbia's ChatGPT Plugin discoverability?
The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.
What are Hebbia alternatives on ChatGPT?
As of 2026-11-03, Hebbia competes with Accredited, Cape Partners, CapitalDart, CB Insights, conclusia, Cookiedeal, Corridome, Dakota Marketplace and 21 more in ChatGPT Private Markets, Deals & Expert Networks, ranked by public Discoverability Score.
Where is this profile measured?
This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.