How big is the FirmStanding employer entity graph right now? Headline aggregates: resolved employer entities and how many appear in 2+ federal sources, states covered, record links by source system (whd, osha, echo, oflc, fmcsa, leie, sam_exclusions), and raw records on file per dataset. [price: $0.001/call]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 full responsibility. It discloses that the tool returns aggregate counts and mentions the call cost ($0.001/call). Since it has no parameters and is clearly a read-only statistics endpoint, the description sufficiently conveys the non-destructive nature. However, it does not explicitly state idempotency or side-effect absence.
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 remarkably concise: two sentences that front-load the core question and then enumerate specific aggregates. Every word serves a purpose, including the pricing note. There is no fluff or repetition.
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 no output schema, the description adequately lists the key output elements (resolved entities, source coverage, record links, raw records). However, it does not specify the return format (e.g., JSON structure) or provide examples. For a simple stats endpoint, this is sufficient but could be slightly more formal.
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 has zero parameters, so the description naturally doesn't need to explain parameter meaning. According to guidelines, baseline for 0 parameters is 4. The description adds value by detailing the output fields, which is more relevant than parameter semantics in this case.
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 purpose: providing aggregate statistics about the FirmStanding employer entity graph. It asks 'How big is the FirmStanding employer entity graph right now?' and enumerates the specific aggregates (resolved entities, sources coverage, record links, raw records). This distinguishes it from sibling tools like firmstanding_company_search that focus on individual companies.
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 for obtaining overall graph statistics but does not explicitly state when to use this tool versus alternatives. No guidance on when not to use or prerequisites is provided. The purpose is self-explanatory, but clarity on exclusion criteria is lacking.
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 domain and specific action (e.g., FDA approvals vs clearances vs recalls; firmstanding business360 dossier vs search vs screen). Even overlapping concepts like 'business360' vs 'business360_lookup' are distinguished by input (UUID vs name+state). No two tools appear to do the same thing.
All tools use a consistent lowercase snake_case pattern with domain prefix (e.g., fda_*, firmstanding_*, fmcsa_*, govcon_*). Action words (search, lookup, screen, feed, stats) follow predictable usage. The naming is uniform and easy to parse.
38 tools is on the higher end but appropriate for a comprehensive government data gateway spanning multiple agencies and datasets. Each domain has a reasonable number of tools (e.g., FMCSA: 7, OFLC: 6). Could potentially be trimmed slightly, but overall well-scoped for the stated purpose.
The tool surface covers the major government data sources comprehensively: FDA (approvals, clearances, recalls), FMCSA (carrier census, safety, insurance, etc.), FSIS, DOJ/OFLC, OSHA/EPA/DOL enforcement, SEC insider filings, clinical trials, VA facilities/opportunities/vendors, and federal contracting. No obvious gaps for the stated gateway purpose.