Skip to main content
Glama

Mesh

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.3/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only and idempotent behavior, and the description adds substantial context beyond that: empty sections mean real 'no data', private companies return resolved:false with a notes line, patent lookups may soft-fail, and companies without biologics will legitimately show no FDA products. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense, and every sentence adds operational value: source list, failure semantics, input forms, and type behavior. It is front-loaded with examples and the preference rule, which makes it easy to scan despite its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of explaining return values, and it does so thoroughly: cik, company_name, resolved_from/to, recent_filings with URIs, fundamentals, sources_used/sources_failed, and expected empty-section behavior. This is enough 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.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and already documents ticker, zero-padded CIK, or company name, plus the interchangeable 'company'/'ticker' values. The description adds some nuance about private-company resolution behavior and 'person/place coming soon', but these are mostly output/limitation notes rather than new parameter-semantic guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a full cross-source profile of a US public company in one parallel call, with concrete example prompts like 'Tell me about X' and 'company profile for Microsoft'. It is distinct from single-lookup tools, but it does not explicitly differentiate from sibling tools such as compare_entities or resolve_entity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear trigger conditions: use when the user asks for a holistic view or says 'tell me about X', and ALWAYS PREFER this over chaining single-pack lookups. It lacks explicit exclusions or direct comparisons with named alternative sibling tools, so it stops short of full when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Mesh toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation3/5

The three ask_pipeworx variants are a genuine confusion risk — ask_pipeworx_beta is currently described as identical to ask_pipeworx, and ask_pipeworx_grounded differs only in extraction strictness. search_descriptors and resolve_term also overlap heavily (both map a term to MeSH descriptor IDs). The six Polymarket tools are differentiated by rich descriptions but still form a dense cluster where misselection is plausible.

Naming Consistency4/5

Most tools follow a clean verb_qualifier pattern (ask_, bet_, compare_, resolve_, search_, validate_) with the brand as a namespace (ask_pipeworx, pipeworx_trending, polymarket_*). Minor deviations exist: pipeworx_feedback and pipeworx_trending lead with a noun, the memory trio (remember, recall, forget) are bare verbs, and the ask_pipeworx family uses a brand name rather than a resource noun — but the overall pattern stays predictable and readable.

Tool Count2/5

At 34 tools this exceeds the threshold where a set feels heavy, and the bloat is visible: three near-identical ask_pipeworx routers and a six-tool Polymarket suite dominate. Several tools (generate_llms_txt, scan_dependency, bet_research) feel like accreted one-offs rather than part of a coherent surface. The broad platform purpose justifies some breadth, but the count is inflated by redundant variants.

Completeness4/5

The core purpose — universal access to authoritative structured data — is well covered via the router, grounded mode, deep_research, entity/profile/compare/validate wrappers, and discovery tools. The subscription lifecycle (subscribe/unsubscribe/list/alerts) and memory (remember/recall/forget) have no dead ends. Minor gaps exist: non-polymarket prediction-market workflows lack the depth of the Polymarket cluster, and the single-purpose niche tools fit awkwardly, but agents won't hit dead ends.

Resources