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Glama

Intercom

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses substantial behavior: fan-out across specific sources, per-source failure modes like USPTO sunset soft-fail and GDELT→GNews fallback, what an empty section means, and the resolved:false/notes behavior for private companies. This goes far beyond what annotations alone provide.

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

Conciseness3/5

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

The description is dense and information-rich, but it is a single long paragraph with several redundant query examples ("research Acme", "brief me on Tesla", "company profile for Microsoft", etc.) that do not each earn their place. It would benefit from restructured bullets or trimming, though the content is relevant.

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?

Despite having no output schema, the description enumerates the key returned sections, clarifies that sources_used/sources_failed indicate per-company reality, and covers input alternatives, failure behavior, and expected empty results. This is complete enough for an agent to call 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.

Parameters4/5

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

The schema already covers both parameters well at 100% coverage, so the baseline is 3. The description adds meaningful extra context: concrete token/CIK/name examples, that `type` values are interchangeable, and how names resolve through SEC EDGAR, including the private-company edge case.

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 states a specific action and resource: building a full cross-source profile of a US public company in one parallel call. The opening examples make the trigger queries unmistakable, and the ALWAYS PREFER directive distinguishes it from chaining single-purpose SEC/XBRL/news lookups.

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?

It explicitly says to use this tool when the user wants a holistic view and to prefer it over chaining single-pack lookups. It also explains acceptable input forms and the private-company fallback, so an agent knows when the tool is appropriate and what to expect when it isn't.

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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route factual questions to the same underlying catalog with unclear boundaries. Polymarket tools also overlap (bet_research, polymarket_edges, polymarket_arbitrage), and ai_visibility_check vs scan_competitor_ai_presence are nearly the same operation.

Naming Consistency3/5

Most tools follow a readable snake_case verb_noun pattern (ask_pipeworx, entity_profile, resolve_entity, list_subscriptions). However, the set mixes two distinct naming families — ic_* for Intercom tools and pipeworx/* for the rest — and includes ambiguous generic names like remember/recall/forget that don't visually connect to the rest.

Tool Count2/5

36 tools is far more than needed for an Intercom-focused server; the vast majority have nothing to do with Intercom and instead cover a sprawling Pipeworx data-research, prediction-market, and memory/subscription toolkit. The count alone is in the 'too many' range, and the scope mismatch makes it feel even more inflated.

Completeness2/5

For a server named Intercom, the surface is severely incomplete: only read/list/search operations exist for contacts and conversations, with no create, update, delete, send-message, or company-detail operations. The unrelated Pipeworx tools are fairly broad, but they don't compensate for the missing Intercom lifecycle coverage that the server name promises.

Resources