edgar-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
The two tools are sharply distinct: one searches full-text across all filings, the other lists filings for a specific company. There is no overlap in purpose or likely misselection.
Naming Consistency5/5Both tool names follow a consistent verb_noun pattern: search_filings and list_company_filings. The convention is uniform and predictable.
Tool Count3/5Two tools is at the thin end of the range, but the server's stated scope of accessing EDGAR filings can reasonably be covered by search and list operations. It feels minimal rather than bloated.
Completeness2/5The tools cover discovery (keyword search and per-company listing) but lack any retrieval tool to actually read a filing's content. Search results intentionally return metadata only, and without a fetch-filing tool, agents cannot access the underlying text, creating a dead end.
Average 4.3/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden and delivers key behavioral traits: it returns metadata only (not matching text, so the URL must be followed) and ranks by keyword relevance rather than company size. These are genuine surprises the agent would not know from annotations or schema. It could add pagination or rate-limit details, but the core quirks are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph that front-loads the core purpose and return shape, then delivers use-case and exclusion guidance, then two critical behavioral caveats. Every sentence adds value; it is slightly long as a wall of text but well-organized and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 6 params, 100% schema coverage, no output schema, and no annotations, the description is comprehensive: it covers the date scope, return format, use case, exclusions, ranking behavior, and the metadata-only caveat. There are no obvious gaps for an agent to safely invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the input schema already documents all 6 parameters including the company lookup behavior and the exact-phrase quote syntax. The description adds no additional parameter-level detail beyond the schema, so the baseline of 3 is appropriate since the structured schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource combination ('Search the full text of SEC filings from 2001 onward') and clearly states what it returns (matching filings with company, form type, date, link). It distinguishes itself from the sibling list_company_filings by emphasizing full-text search across all filings vs. what appears to be a per-company listing function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use it ('find how public companies discuss a topic in their own words') and provides clear exclusions ('Do not use for stock prices, financial figures, or private companies'). It lacks an explicit pointer to the sibling alternative tool, but the use-case framing and negative guidance are strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It discloses ordering (newest first), internal ticker/company resolution, and the fact that unfiltered results are dominated by Form 4 filings. It does not explicitly state read-only behavior or return format, but 'List' makes this reasonably clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Four concise sentences, each with a purpose: action/scope, usage context, company resolution, and filtering guidance. No wasted words, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with three well-documented parameters, the description covers selection criteria, invocation behavior, and filtering advice. It does not describe return fields, but with no output schema the agent can still reasonably infer it returns a list of filings. Slightly more detail on return shape would make it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents company, forms, and limit, including the Form 4 warning. The description reinforces the ticker/company resolution and filter-by-form guidance, but does not add substantively new parameter semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'List filings a specific public company submitted, newest first,' which clearly identifies the resource and operation. It also distinguishes itself from the sibling search_filings by contrasting recent-reports-by-company with word-based searching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use the tool: 'Use when the user names a company and wants its recent reports rather than searching for particular words.' It also gives practical guidance on filtering by form type, which helps the agent decide how to invoke it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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