coverageCorpus coverage: which SEC datasets exist, 10-K backfill progress by quarter (2015→now), and — given a ticker — which fiscal years are on file for it. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | No | Optional stock ticker to check company-specific 10-K coverage |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It transparently lists the data scope (SEC datasets, 10-K backfill progress, per-ticker fiscal years) and notes the tool is 'Free', which is a behavioral trait. It doesn't mention limitations or return format, but for an informational 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 concise, using a compact structure with an em dash to list the three coverage aspects. Every phrase adds value, no filler or redundancy. It is front-loaded with the core concept ('Corpus coverage') and efficiently details what it provides.
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 simplicity (one optional parameter, no output schema), the description is adequately complete. It covers the main functionality and the 'Free' note adds cost context. However, it could mention the return format or any typical use cases, but these are not critical for a tool of this scope.
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 baseline is 3. The description adds meaning by explaining what the 'ticker' parameter does ('given a ticker — which fiscal years are on file for it'), reinforcing and expanding upon the schema's description. This extra context justifies a 4.
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 specifies what the tool does: it provides corpus coverage information, listing specific aspects (SEC datasets, 10-K backfill progress, per-ticker fiscal years). This distinguishes it from sibling tools like find_data or pricing, which have different purposes.
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 implied usage is clear: use this tool when needing to understand what SEC data is available and for checking a ticker's coverage. However, it doesn't explicitly mention when NOT to use it or offer alternatives, so it falls short of full contextual guidance.
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.