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Glama

Climatiq

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

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses meaningful runtime behavior: it fans out across many sources in parallel, soft-fails for sunset APIs, uses GDELT→GNews fallback, returns resolved:false for private companies, and explains that empty sections are real 'no data' rather than failures. It also notes that missing FDA-licensed biologics is expected for small-molecule/generic companies. This is strong behavioral disclosure that goes well beyond the structured hints.

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 every sentence earns its place: examples are front-loaded, the source list and return fields are compactly organized, and caveats are attached to the relevant sections. For a tool with no output schema, this density is justified and well structured rather than verbose.

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?

Given the lack of an output schema and the high complexity of a multi-source entity profile, the description is remarkably complete: it lists all upstream sources, each returned field with caveats, the input formats, failure semantics, and how to interpret empty sections. An agent has enough context to invoke the tool correctly and interpret its results without additional documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds practical semantics: both type values are interchangeable, value can be a ticker, zero-padded CIK, or company name, and names resolve via SEC EDGAR. It also clarifies the resolved-from/resolved-to behavior and the private-company resolution failure mode, which are valuable beyond the raw schema.

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

Purpose5/5

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

The description opens with concrete user phrasings and names the resource precisely: 'full cross-source profile of a US public company in ONE parallel call.' It clearly differentiates itself from sibling tools like compare_entities and deep_research by defining entity_profile as the single-call holistic company lookup, and from resolve_entity via the profiling behavior. A specific verb, resource, and scope are all present.

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

Usage Guidelines5/5

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

The description includes an explicit routing directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also details input forms, the private-company behavior, and the type/value interchangeability, so an agent knows when to invoke this tool and what to expect. It names the alternative approach (chaining lookups) and the conditions that select this tool instead.

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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TDQS

A4/5.0
Disambiguation3/5

Most tools have distinct jobs and the descriptions are unusually detailed with cross-references, but there is real overlap in the ask_pipeworx family (ask_pipeworx_beta is explicitly identical to ask_pipeworx right now), the Polymarket edge/arbitrage cluster, and some company-research tools. An agent can usually pick correctly, but only after reading long descriptions carefully.

Naming Consistency3/5

All names are snake_case and many are clear verb_noun forms like estimate_emissions or list_subscriptions, but the set also contains descriptive noun phrases (entity_profile, recent_changes, ai_visibility_check), brand-prefixed names (pipeworx_feedback, polymarket_edges), and bare memory verbs (remember, recall, forget). This is a readable but mixed convention rather than one predictable pattern.

Tool Count2/5

34 tools is well above the comfortable ceiling, and the set bundles several unrelated domains: Climatiq emissions, Pipeworx data research, prediction-market analytics, memory, subscriptions, AI visibility, llms.txt generation, and npm dependency checks. It feels heavy and redundant, with ask_pipeworx_beta and the AI-visibility pair as candidates for removal or merging.

Completeness4/5

The core query workflows are well covered: emission factors lead into estimation, general lookups have plain/grounded/deep variants, claim validation and entity profiles exist, and subscriptions/memory have full lifecycles. The main gaps are minor—no batch emissions endpoint or direct order execution—so agents can work around them.

Resources