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mathjs

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (read-only, idempotent), the description discloses fallback behavior when GLEIF/OpenFIGI are unavailable, that unresolved identifiers appear under 'unresolved' rather than being omitted, that ambiguous FIGI matches return 'figi_candidates,' and that each call cascades through multiple endpoints. This materially exceeds the annotations' safety hints.

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 front-loaded with query examples and the 'Use FIRST' directive, and almost every sentence carries substantive information. It is long and dense, with run-on parentheticals in the company section, but little content is wasted given the tool's complexity.

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 compensates by detailing output components: CIK, ticker, company_name, LEI with ownership, FIGI, figi_candidates, unresolved, RxCUI, ingredient, brand, and citation link. It also covers input coverage, failure modes, and graceful degradation, so an agent has what it needs to invoke and interpret results.

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

Parameters5/5

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

The description and schema's value description add substantial meaning beyond the bare type/enum: accepted input formats for company (ticker, CIK, ISIN, name), drug examples, and a critical caveat to pass only the entity name and not the full noun phrase. The 100% schema coverage is reinforced, not merely repeated.

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 uses a specific verb ('resolve') with a clear object: converting user-spoken names into canonical/official identifiers for companies and drugs. It enumerates supported types, identifier sources, and includes example phrasings, making the tool's purpose unmistakable and distinct from sibling tools that profile or compare entities.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' which routes an agent to this tool over lookalikes. It also gives rich when-to-use context through example queries, though it does not name sibling alternatives or state when not to use it.

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.

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