Get a derived 'company health' grade (A-E) for a UK company from status, charges, and filing signals. Differentiator vs raw register. Heuristic, not financial advice.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | UK company number (e.g. 00002078) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It reveals that the grade is 'derived' and 'heuristic', which indicates an approximate calculation, and adds 'not financial advice' as a disclaimer. This goes beyond just the function name, though it doesn't detail the exact heuristic or return format beyond the grade.
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, front-loaded with the primary action, and every clause adds value: the purpose, the differentiating factor, and the heuristic disclaimer. No redundant 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 simple tool with one parameter and no output schema, the description provides adequate context: what it returns (A-E grade), how it's derived (from status, charges, filings), and its limitations (heuristic, not financial advice). It could mention the output structure more explicitly but remains reasonably complete.
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 input schema already has 100% coverage for the single parameter 'number' with an example. The description doesn't add any extra parameter semantics, which is acceptable given the schema's completeness. Baseline 3 applies.
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 specific function: 'Get a derived company health grade (A-E) for a UK company from status, charges, and filing signals.' It uses an action verb and resource, and explicitly differentiates itself from raw register data, distinguishing it from sibling tools like uk_company_profile or uk_company_charges.
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 phrase 'Differentiator vs raw register' tells the user when to use this tool (for a derived grade) versus alternatives that provide raw data. The caveat 'Heuristic, not financial advice' also guides appropriate usage. However, it doesn't explicitly name alternative tools or provide exclusion criteria, so it's clear but not exhaustive.
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.
Several tool groups have overlapping purposes: fda_drug_labels vs health_drug_search, fda_recalls vs health_recalls, fx_official_rates vs treasury_fx_rates, treasury_debt vs us_debt_current, and get_gdp vs get_bea_gdp. These near-duplicates create real ambiguity for an agent deciding which tool to call.
Names mix verb-led styles (get_, search_, compare_, screen_) with domain-led styles (fx_, treasury_, uk_, health_, eurostat_, datausa_). Within the same domain, similar actions use different patterns (get_gdp vs eurostat_gdp vs imf_indicator), making the set feel inconsistent and hard to predict.
75 tools is extreme for any MCP server, especially when many tools are redundant or cover unrelated domains (weather, earthquakes, scholarly search, air quality) outside the stated SEC/economics/demographics/FX focus. This overwhelms agents and burdens context windows.
Core domains like SEC financials, major economic indicators, basic demographics, and current FX rates are well covered. However, gaps remain: no historical FX rates, no stock price/quote tool, limited demographic breakdowns, and no ability to fetch full SEC filing text. Some operations end in dead ends.