This server provides access to institutional 13F filing data (SEC-mandated quarterly disclosures) from the13f.com, enabling financial research on hedge fund and institutional manager portfolios without writing HTTP code.
List Available Quarters (list_quarters): Retrieve all quarters with 13F holdings data, including the most recent quarter.
Search Managers (search_managers): Find 13F filers by name via case-insensitive substring search (e.g., "berkshire" → Berkshire Hathaway).
Get Manager Holdings (get_manager_holdings): Fetch the full position list for a specific manager (by SEC CIK) for a given quarter.
Bulk Holdings Lookup (get_manager_holdings_bulk): Retrieve holdings for up to 25 (CIK, quarter) pairs in a single call — useful for comparing managers or tracking one over time.
List All Managers (list_all_managers): Access the full universe of 8,600+ 13F filers with AUM and summary stats, optionally filtered to active managers only.
Find Similar Managers (find_similar_managers): Match a custom portfolio (tickers + optional weights) against the institutional universe to find the most similar or most different managers.
Get Consensus Portfolio (get_consensus_portfolio): Identify the top most-widely-held securities across all institutional managers for a given quarter.
Get Market Regime (get_market_regime): Retrieve an institutional sentiment snapshot including the IIOI composite score, regime state (e.g., "High Optimism"), and transition signals.
Get Sector Flows (get_sector_flows): Analyze per-sector institutional capital flows and risk posture for a given quarter, with optional filtering by GICS sector.
Model Context Protocol server for the13f. Brings institutional 13F intelligence into Claude Desktop, Cursor, VS Code + Continue, and any MCP-compatible host — no HTTP code required on your side.
Research data only. Sourced from publicly disclosed SEC Form 13F filings,
which lag quarter-end by up to 45 days. Past institutional positioning does
not predict future performance. Nothing here constitutes investment advice.
Every tool's response includes a disclaimer field; the host LLM should
surface it alongside any analysis it produces.
Related MCP server: ROIC.ai Financial Data MCP Server
uvx the13f-mcpOr pip install the13f-mcp if you prefer to manage Python environments
yourself. Python 3.11+ required.
Free, no payment, no credit card:
Enter your email and solve the Cloudflare Turnstile challenge
Copy your pf13f_ key — shown once
Paste it into your MCP host's config as THE13F_API_KEY (see below)
Restart the host
Free tier: 100 read calls per day; quota rolls at UTC midnight. Signal and report tools arrive in later minor versions when paid tiers launch.
Edit ~/Library/Application Support/Claude/claude_desktop_config.json
(macOS) or %APPDATA%/Claude/claude_desktop_config.json (Windows):
{
"mcpServers": {
"the13f": {
"command": "uvx",
"args": ["the13f-mcp"],
"env": {
"THE13F_API_KEY": "pf13f_..."
}
}
}
}Restart Claude Desktop. The13f tools should appear in the MCP tool list.
Settings → MCP → Add Server. Paste the same JSON block into the "Custom" field.
Settings → experimental.modelContextProtocolServers → add a stdio
transport running uvx the13f-mcp with THE13F_API_KEY in env. The key
reveal page at https://the13f.com/developers/signup-success shows a
copy-paste-ready snippet with the key already filled in.
Tool | What it does |
| All quarters with holdings data + the latest quarter |
| Autocomplete 13F filers by name |
| Full positions for a CIK + quarter |
| Up to 25 |
| Universe of 8,600+ filers with per-manager summary stats |
| Match a portfolio against the universe |
| Most-widely-held securities per quarter |
| Institutional regime snapshot (IIOI composite, state, transition) |
| Per-sector capital flows and risk posture |
Version | Tools | Requires |
v0.2.0 |
| Standard tier subscription |
v0.3.0 |
| Standard tier + stored card; two-step confirm |
The free-tier Read tools are enough to explore the data and build prompts against it. Signal and report tools deliberately wait on the paid-tier billing plumbing.
"Pull Berkshire Hathaway's top 20 positions as of Q4 2025 and summarize what grew the most quarter-over-quarter."
"I own AAPL, MSFT, and NVDA equal-weight. Find the 5 institutional managers whose portfolios most closely resemble mine."
"What sector did 13F filers most aggressively reduce last quarter? Show me the top three managers leading the reduction."
The MCP host's LLM picks the right tool, fills in arguments, surfaces the
disclaimer, and returns the result as structured JSON.
Var | Default | What it controls |
| (none) | Your |
|
| Override for local development against a running copy of the13f's gui_server. |
|
| Per-request HTTP timeout in seconds. Minimum 5. |
Public source: https://github.com/pickelfintech/the13f-mcp. This is the
snapshot that PyPI, Glama.ai, and the MCP community list point at. The
GitHub repo is a push-mirror of the GitLab one at
gitlab.com/pickel-fintech/the13f-mcp; GitLab remains the primary and
also carries the release CI. Bug reports and PRs are accepted on either
side.
MIT — see LICENSE. The hosted api.the13f.com API that this
client calls is a separate service. A free API key (100 calls/day) is
required; sign up at https://the13f.com/developers/.
tom@pickelfintech.com — quote the request_id field returned by any failing
tool call for fastest triage.
MIT. See LICENSE. The MCP server source is MIT-licensed; use of the the13f API itself is governed by the Terms of Service at https://the13f.com/terms.html.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Match a portfolio against the 13F universe and return the closest (or deliberately-different) managers.
Args: holdings: list of {"ticker": "AAPL", "weight": 0.05}; weight optional (equal weights default). quarter: defaults to latest quarter if None. top_n: 1-50, default 10. direction: "similar" (closest matches) or "unlike" (most different).
Returns: {"quarter": "...", "matches": [{"cik": ..., "similarity_score": ..., ...}, ...], "disclaimer": "..."}
| Name | Required | Description | Default |
|---|---|---|---|
| holdings | Yes | ||
| quarter | No | ||
| top_n | No | ||
| direction | No | similar |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. It clearly explains that returns are disclosed institutional positions, not manager intent, and that direction ('similar' or 'unlike') controls behavior. Lacks details on edge cases or performance, but is adequate for 4 params.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with clear sections: research warning, purpose, args in bullet-like format, and returns. No unnecessary words, front-loaded with important caveat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 params, no output schema, and no annotations, the description adequately covers inputs and output structure. Returns object described with fields and disclaimer. A minor gap: no mention of error handling or data source limitations beyond the explicit warning.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description explains each parameter: holdings with example format (ticker and weight), quarter defaults to latest, top_n range, direction enum with meanings. Adds value by clarifying weight optional and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool matches a portfolio against the 13F universe and returns closest or deliberately-different managers, specifying verb (match) and resource (13F universe). It also distinguishes from siblings like get_manager_holdings by focusing on similarity search, not individual holdings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Research data only' and warns about inferring manager intent, indicating when to use. However, it does not explicitly contrast with alternatives like search_managers or get_consensus_portfolio, though the unique purpose is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Return the top-N consensus portfolio - securities held by the most institutional managers.
Args: quarter: quarter code (e.g. "q4y2025").
Returns: {"quarter": "...", "count": 50, "securities": [{"ticker": ..., "weight_pct": ..., ...}, ...]}
| Name | Required | Description | Default |
|---|---|---|---|
| quarter | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries the full burden. It clearly states the tool is for research only, returns historical positions without inference of intent. It also specifies the output includes top-N securities with weight percentages.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two sentences plus an args/returns block. It front-loads the key caveat 'Research data only'. Every sentence adds value, though the returns example could be slightly more terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema and only one parameter, the description adequately explains the tool: what it returns (securities, ticker, weight), the quarter format, and the top-N nature. The research caveat adds needed context. Not perfect, but sufficient for this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% coverage but only one parameter quarter, and the description includes an example format 'q4y2025'. This adds semantic meaning beyond the schema's type: string. The return value example also clarifies the format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb+resource: 'Return the top-N consensus portfolio - securities held by the most institutional managers'. It clearly distinguishes from siblings by stating 'Research data only' and listing sibling tools like get_manager_holdings which focus on individual managers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a clear disclaimer: 'Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction.' This guides the agent on limitations. However, it does not explicitly state when to use this vs alternatives, though the sibling context implies use for broad consensus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Return all positions held by a 13F filer in a given quarter.
Args: cik: zero-padded 10-digit SEC CIK (e.g. "0001037389"). quarter: quarter code like "q4y2025".
Returns: { "cik": "...", "quarter": "...", "n_positions": 3185, "total_value_thousands": 64461244358.0, "holdings": [{"CUSIP": ..., "TICKER": ..., "VALUE": ..., ...}, ...], "disclaimer": "..." }
| Name | Required | Description | Default |
|---|---|---|---|
| cik | Yes | Zero-padded 10-digit SEC CIK. | |
| quarter | Yes | e.g. q4y2025 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It highlights that this is research-only data and warns about intent inference, which adds useful context, but does not discuss mutation side effects, rate limits, or data freshness implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear Args and Returns sections, and is moderately concise. The first sentence adds critical context about the tool's limitations, though the return example is somewhat verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 params, 100% schema coverage, no nested objects), the description provides sufficient context including a clear return example and usage caveats. No output schema exists, so the return format description is valuable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents both parameters. The description provides the exact format for cik (zero-padded 10-digit) and a quarter example, but adds little beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns disclosed institutional positions for a 13F filer in a given quarter, distinguishing it from siblings like find_similar_managers and get_consensus_portfolio.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description notes 'Research data only' and warns against inferring intent, but does not explicitly state when to use this tool versus alternatives like get_manager_holdings_bulk or get_consensus_portfolio.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Look up holdings for many (cik, quarter) pairs in one call (Tier 3).
More efficient than calling get_manager_holdings repeatedly: groups by quarter so each parquet file is read at most once. Single billing event regardless of how many pairs you supply (up to 25 per call).
Args: pairs: list of {"cik": "...", "quarter": "..."} dicts. Min 1, max 25.
Returns: { "results": [ {"cik": "0001067983", "quarter": "q4y2025", "status": "ok", "n_positions": 42, "total_value_thousands": ..., "holdings": [...]}, {"cik": "0001234567", "quarter": "q4y2025", "status": "not_found", "error": "..."}, ... ], "n_requested": 5, "n_ok": 4, "n_failed": 1 }
Per-pair errors do NOT raise; check status field on each result. Use this when you need holdings for multiple managers/quarters at once (e.g. comparing a portfolio across periods or surveying peer institutions).
| Name | Required | Description | Default |
|---|---|---|---|
| pairs | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly discloses that this is a Tier 3 call, groups by quarter for efficiency, and results include both success and failure statuses per pair without raising errors. A thorough picture is given, though slightly more detail on rate limits or data freshness could push to 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description begins with a short, direct summary, followed by efficiency rationale, parameter explanation, and return format. Each sentence adds value; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lack of output schema, the description covers input, output, behavior, error handling, and usage context. It is complete for a bulk lookup tool of moderate complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The detailed docstring explains the pairs parameter format, constraints (min/max), and expected structure. It also describes the return format, including the per-item response with fields like status, n_positions, total_value_thousands, and holdings. Since schema coverage is 0%, the description compensates fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves disclosed institutional holdings for multiple (cik, quarter) pairs at once, distinguishing it from the single-pair sibling get_manager_holdings. It specifies research-only data and cautions against inferring intent, making the purpose precise and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use this tool: when you need multiple holdings, comparing portfolios across periods, or surveying peer institutions, as it is more efficient than repeated single calls. It also sets expectations by noting per-pair errors do not raise exceptions, so the consumer must check the status field.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Return the institutional regime snapshot for a quarter (IIOI composite, regime state, transition).
Args: quarter: quarter code.
Returns: {"quarter": "...", "iioi_composite": 62.4, "regime_state": "High Optimism", ...}
| Name | Required | Description | Default |
|---|---|---|---|
| quarter | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that data reflects institutional positions and should not be interpreted as intent, which is helpful behavioral context beyond mere operation. No annotations exist, so description compensates well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise: two sentences plus an Args/Returns section. No wasted words, clearly front-loaded with key purpose and caveat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple schema (1 param, no output schema, no enums), the description provides enough context: purpose, caveat, and return example. However, lacks details on quarter format or sources but sufficient for a straightforward tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'quarter: quarter code' which adds minimal meaning beyond the schema (which has no description). With 0% schema coverage, the description partially compensates but lacks detail on format or valid quarter codes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states tool returns institutional regime snapshot for a quarter, with clear verb 'Return' and resource 'institutional regime snapshot'. It distinguishes itself from siblings like list_quarters or search_managers by focusing on regime state data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explicitly says 'Research data only' and warns against inferring manager intent, which guides appropriate use. However, it does not explicitly state when to use this vs alternatives or exclude cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Return per-sector institutional capital flow intelligence for a quarter.
Args: quarter: quarter code. sector: optional GICS sector to filter (e.g. "Information Technology").
Returns: {"quarter": "...", "sectors": [{"gics_sector": ..., "net_capital_flow_thousands": ..., "risk_posture": ..., ...}, ...]}
| Name | Required | Description | Default |
|---|---|---|---|
| quarter | Yes | ||
| sector | No | GICS sector name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, making the description the sole source for behavioral disclosure. It appropriately identifies the data as research-only, returns institutional positions, and cautions against inferring intent. However, it does not mention whether the tool is a simple read operation or has any side effects, rate limits, or pagination behavior. With no annotations, the description provides adequate transparency but could be more comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a brief introduction, argument descriptions in a list, and a return format example. It is concise and front-loaded with the critical warning about data limitations. The only improvements would be to remove the redundant 'Research data only' phrase if the same information is already in the tool title (but title is null), so it earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (2 parameters, no output schema), the description covers the purpose, the data nature, and the return format with an example. It is largely complete for a straightforward query tool. However, it does not explain what 'risk_posture' means in the output, which would be helpful. Sibling tools are not explicitly differentiated, but the purpose is distinct enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the input schema by explaining that 'quarter' is a quarter code and 'sector' is an optional GICS sector filter with an example. Schema coverage is 50% (only sector has a description), but the description compensates by providing context for both parameters. It does not list valid quarter codes or explain the format, which would be helpful, but the combination of schema and description is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns per-sector institutional capital flow intelligence for a quarter, specifying the data is research-only and should not be used to infer manager intent. The verb 'get' combined with 'sector flows' makes the tool's purpose unambiguous and distinguishes it from sibling tools like 'get_manager_holdings' which focus on individual managers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly warns about data limitations ('do not infer manager intent or future direction') and indicates this is for research purposes. It mentions an optional GICS sector filter, but does not explicitly contrast with sibling tools or specify when to use alternatives like 'get_consensus_portfolio'. The usage context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Return the full universe of 13F filers with per-manager AUM.
Args: active_only: if true (default), only managers present in the latest quarter.
Returns: { "count": 8625, "active_only": true, "managers": [{"cik": "...", "name": "...", "latest_aum": ..., ...}, ...], "disclaimer": "..." }
NOTE: response can be ~6 MB JSON; consider filtering client-side.
| Name | Required | Description | Default |
|---|---|---|---|
| active_only | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description warns about response size (~6 MB) and includes a disclaimer. No annotations exist, so the description partially fulfills behavioral transparency, but it does not detail any destructive behavior or authorization needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with clear sections for description, args, returns, and a note. Each sentence serves a purpose, but the format could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter, no output schema, and no annotations, the description is complete: it explains purpose, parameter, return format, and a size warning. Enough for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains the single parameter 'active_only' and its default behavior clearly, adding value beyond schema. The Returns section also adds clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool returns 'the full universe of 13F filers with per-manager AUM', a specific verb+resource. It distinguishes from sibling tools like 'search_managers' by offering a complete list rather than filtered search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clarifies that data is for research only and cautions against inferring intent. It provides a note on response size, suggesting client-side filtering. However, it does not explicitly contrast with sibling tools or state when to use alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Return all quarter codes with available data.
Returns: {"quarters": ["q1y2013", ..., "q4y2025"], "latest": "q4y2025"}
Use the latest quarter by default unless the user specifies one.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description carries the burden and does well: it warns the data is research-only and not to infer manager intent, which is crucial behavioral context that goes beyond the simple listing function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with three short sentences, each serving a distinct purpose: data caveat, output format, and usage instruction. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool with no output schema, the description fully covers purpose, behavioral caveats, output format, and default usage. It is complete for its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the description explains the output format and default behavior, adding value beyond the empty schema. The baseline is 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns quarter codes with available data, specifying the exact output format and default behavior. It distinguishes itself from siblings by being a simple listing tool with no input parameters, unlike other tools that require manager names or filters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises to use the latest quarter by default unless the user specifies one, which provides clear usage guidance. However, it does not explicitly mention when not to use this tool or alternatives, though its purpose is distinct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research data only. Returns disclosed institutional positions; do not infer manager intent or future direction. Find institutional 13F filers by name (case-insensitive substring, min 2 chars).
Args: query: substring of the manager's name (e.g. "renaissance", "berkshire"). limit: max results to return, 1-50, default 10.
Returns: { "query": "...", "results": [ {"cik": "0001037389", "name": "Renaissance Technologies Llc", "latest_aum": 64461244358, "available_quarters": [...]}, ... ], "disclaimer": "Research data only..." }
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Substring of the manager's name. | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly states the data limitation (research data only) and the constraint that positions are disclosed, not reflecting intent. This is a strong disclosure for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is brief but front-loaded with the critical caveat. It uses clear sections for args and returns. Slightly more verbose than necessary but still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with only 2 parameters and no output schema, the description covers the data disclaimer, parameter semantics, and return structure adequately. No output schema is needed as description details the response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (one described, one not). Description adds examples for 'query' and explains 'limit' range and default, going beyond the schema. However, both parameters are simple, and the schema already covers constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it searches institutional 13F filers by name, with case-insensitive substring search and minimum 2 chars. Distinguishes from siblings like 'find_similar_managers' and 'list_all_managers' by specifying the search scope and data constraints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly warns that data is research-only and not to infer intent. However, it does not contrast with siblings (e.g., when to use this vs 'find_similar_managers') nor specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a distinct operation: searching managers, retrieving holdings, comparing portfolios, getting consensus data, sector flows, regime states, and quarter listings. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern (find_, get_, list_, search_) with clear action and domain. The only minor deviation is 'find_similar_managers' being more specific than others, but it remains readable.
With 9 tools, the server covers a well-scoped domain of 13F data analysis including lookups, comparisons, and market overview. Each tool serves a clear purpose without bloat.
The tool set covers key workflows: manager search, holdings retrieval (single and bulk), portfolio comparison, consensus portfolio, sector flows, and market regime. A minor gap is the lack of a tool to retrieve historical quarter-by-quarter changes for a single manager, but the core CRUD and analysis operations are present.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Global stock research, ML forecasts, valuation signals, screeners & portfolio tracking in Claude
Real SEC, 13F, insider, congress & macro data your AI agent can cite. Hosted MCP, 24 tools.
SEC filings for Claude, ChatGPT, Cursor — 23 tools + 5 recipes. Financials, insider, 13F, funds.
Portfolio analytics + US-equity market research for AI clients. ChatGPT deep-research compat.
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