find_dataDescribe the data you need in plain language (e.g. 'Apple risk factors 2023', 'is this token a honeypot', 'is this email deliverable', 'read this page'). Searches this server's datasets first, then the whole Professor Sausages catalog, and returns matching endpoints with method, URL, price, and how to call them. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you're trying to find or do, in your own words |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the search order (server datasets first, then catalog), the return structure (method, URL, price, call instructions), and the 'Free' pricing. It does not mention potential failure modes or whether any side effects occur, but for a search tool this is sufficient.
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 two sentences: the first gives the instruction with examples, the second explains the search behavior and output. No word is wasted, and key information is front-loaded.
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 single-parameter discovery tool with no output schema, the description explains what the user should provide, how the search works, and what the response will contain. This is complete for an agent to invoke the tool correctly and interpret results.
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 parameter 'task' is already documented. The description adds value by explicitly instructing users to use plain language and by providing multiple diverse examples that clarify the expected input format. This goes beyond the schema's basic description.
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's function: it searches the server's datasets and the Professor Sausages catalog to return matching endpoints with method, URL, price, and calling instructions. This verb+resource structure distinguishes it from sibling tools that provide specific data (e.g., fx_rate, ipo_calendar).
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 implies usage by instructing users to describe their data needs in plain language and provides varied examples (e.g., 'Apple risk factors 2023', 'is this token a honeypot'), showing it is a discovery tool. It lacks explicit exclusions or directions to use specific sibling tools when already known, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and operation: coverage and pricing are meta, find_data searches endpoints, request_data handles missing data, and the data tools are clearly separated by type (FX, holdings, insider, IPO, macro, SEC filings, security holders). Even the three holdings-related tools have clear boundaries: manager_holdings gives a portfolio, holdings_changes gives changes vs prior quarter, and security_holders gives holders by CUSIP.
All names use lowercase with underscores, which is consistent, but the pattern mixes nouns (coverage, pricing, fx_rate, macro_series) and verb_noun pairs (find_data, request_data). This is readable but not a fully predictable verb_noun convention as seen in well-structured servers.
With 12 tools, the count is well within the ideal range for a data-access server, covering discovery, metadata, pricing, and a broad set of financial datasets without feeling bloated.
The domain is financial data access, and it covers key areas: SEC filings, institutional holdings, insider activity, IPO pipeline, macroeconomic series, FX rates, and data discovery. The main gap is a lack of a full-text filing retrieval tool, but sec_filing_section provides sections, and request_data allows filling missing coverage.