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Glama

mathjs

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds substantial behavioral context: USPTO PatentsView sunset soft-fail, expected empty fda_products for non-biologic companies, empty sections meaning real 'no data' not bugs, private companies returning resolved:false with notes, and sources_used/sources_failed behavior. These are exactly the non-obvious behaviors an agent needs to interpret results correctly.

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

Conciseness4/5

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

The description is long but unusually information-dense, and it front-loads trigger phrases and a high-level purpose before diving into output fields and edge cases. A bulleted structure would improve scannability, but every sentence adds real operational value, so the length is justified.

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?

There is no output schema, so the description correctly carries the burden of explaining return shape. It enumerates all returned sections, explains sources_used/sources_failed, clarifies when an absence of data is expected versus a bug, and covers accepted input forms and failure modes. For a tool this complex, the description leaves little essential context missing.

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?

The input schema already documents both parameters with 100% coverage, including the accepted value shapes and the interchangeability of type values. The description mostly repeats those examples and adds behavioral detail like zero-padded CIK and name resolution, which is useful but does not substantially change the parameter semantics beyond the 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 defines the tool as a 'full cross-source profile of a US public company in ONE parallel call.' It clearly identifies the resource (US public company) and the action (profile/brief/research), and it distinguishes itself from single-pack SEC/XBRL/news lookups by saying it should be preferred over chaining them.

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?

It gives explicit when-to-use signals ('Tell me about X' / 'research Acme' / 'brief me on Tesla') and an explicit preference rule over chained single-source lookups. It does not name sibling tools like compare_entities or deep_research as alternatives, but it provides enough context for an agent to know when this is the right holistic-profile tool.

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the ask_pipeworx family (4 variants) and polymarket tools (5 variants). The memory tools (remember/recall/forget) and subscription tools also overlap with each other. While descriptions provide some differentiation, the sheer number of similar tools makes it hard for an agent to quickly distinguish the right one.

Naming Consistency2/5

Most names use snake_case, but there is no consistent verb_noun pattern. Some are verb_noun (ask_pipeworx, resolve_entity), some are noun_noun (bet_research, entity_profile), and others are adjective_noun (recent_changes, pipeworx_trending). The naming is arbitrary and doesn't follow a predictable convention.

Tool Count2/5

At 33 tools, the count is high but not extreme for a broad data platform. However, the server is named 'mathjs', implying a math focus, yet only 2 tools (evaluate, convert_units) are math-related. The vast majority of tools belong to a completely different domain (data lookups, prediction markets, subscriptions), making the count inappropriate for the server's apparent purpose.

Completeness1/5

For a math server, the tool surface is severely incomplete—missing basic operations like plotting, equation solving, calculus, etc. For the actual data integration and prediction market functionality, the set is more complete, but the server name misleads. The mismatch between name and content makes the completeness score very low based on the implied domain.

Resources