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Glama

wikifeed

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?

Even with annotations marking readOnly, openWorld, idempotent, and non-destructive behavior, the description adds substantial behavioral context: internal cascading through lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous-result handling via figi_candidates, explicit unresolved identifiers, and source-labelling. This gives the agent accurate expectations beyond what annotations alone convey.

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 dense and long, but most content earns its place by covering important edge cases and behavioral nuances. The opening examples and 'Use FIRST' directive front-load the core purpose. Some internal sentences are overflowing with parenthetical detail, but the structure is still navigable and information-rich.

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 tool has no output schema and a non-trivial cascading lookup behavior, the description is remarkably complete. It explains supported types, identifier sources, failure modes, ambiguous outcomes, and degradation behavior. An agent has enough context to call the tool correctly and interpret its results in most realistic scenarios.

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?

Although the schema covers both parameters with descriptions, the tool description adds significantly more meaning: accepted input formats for company identifiers, the distinction between issuer and instrument names for bonds, the warning against including trailing security-class words, and the ISIN-to-legal-entity behavior. This materially improves how an agent would populate the value parameter.

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 verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates supported entity types and example phrasings, and clearly distinguishes itself from sibling tools like entity_profile or compare_entities by positioning itself as the identifier-lookup step. This is far beyond a vague or tautological statement.

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 explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains what kinds of names and identifiers are accepted, including edge cases like non-equity instruments. It does not name alternative sibling tools to exclude, but the direct instruction to use FIRST and the detailed supported-type scoping provide strong usage context.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists: ask_pipeworx and ask_pipeworx_grounded are very similar (one grounded), and multiple Polymarket tools (arbitrage, edges, tracker, fill_risk, kalshi_spread) could be confused despite distinct roles. Overall, an agent can usually differentiate with careful reading.

Naming Consistency3/5

Tool names follow mixed conventions: some use verb_noun (ask_pipeworx, compare_entities), others start with prefixes (pipeworx_, polymarket_, scan_), and a few are nouns (most_read, on_this_day). While readable, there is no consistent pattern, making it harder to predict tool names.

Tool Count3/5

34 tools is high but not extreme for a broad-scope server. However, the server name 'wikifeed' suggests a Wikipedia focus, yet only 4 of 34 tools relate to Wikipedia (featured_article, most_read, on_this_day, picture_of_day). The tool count feels excessive relative to the name, but the actual breadth may justify it.

Completeness4/5

The toolset covers a wide range of domains (company data, prediction markets, factual queries, Wikipedia) with reasonable depth. Minor gaps exist: no Wikipedia search or edit tools, no direct tool for simple web search (relying on ask_pipeworx). Overall, agents can accomplish most tasks without hitting dead ends.

Resources