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Glama

Corporate Apology

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

A4.5/5.0
Behavior5/5

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

Annotations already establish read-only and idempotent behavior, and the description adds substantial behavioral context: internal cascading through lookup endpoints, graceful degradation of LEI/FIGI enrichment, ISIN-to-LEI mapping for non-US issuers, explicit reporting of unresolved identifiers, and ambiguity handling via figi_candidates. This goes far beyond the annotations and there is no contradiction.

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 information-dense and structured with SUPPORTED TYPES, and the key usage rule is front-loaded. It is quite long and contains nested caveats, but nearly every sentence carries operational value; a bit of tightening would improve scannability without losing meaning.

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 carries the full burden of explaining return behavior. It covers identifier provenance labeling, explicit unresolved fields, figi_candidates ambiguity behavior, RxCUI citation format, and failure-mode degradation. Both entity types, accepted inputs, and edge cases are addressed, making it complete for invoking the tool correctly.

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 schema already documents both parameters at 100% coverage, and the description still adds meaningful semantics: company value accepts ticker, CIK, ISIN, or name; drug accepts brand or generic name; and for bonds the issuer must be passed exactly as printed, with a warning against trailing security-class words. This materially helps an agent supply correct values.

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

Purpose4/5

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

The description opens with concrete user utterances and then states the tool resolves a user-spoken NAME to canonical/official identifiers, naming supported types 'company' and 'drug'. It clearly signals it is the ID-lookup entry point ('Use FIRST whenever you have a name but need an ID'), but it never explicitly names a sibling alternative such as entity_profile, so differentiation is implied rather than stated.

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 an explicit trigger condition: 'Use FIRST whenever you have a name but need an ID.' It also describes ambiguous cases where the tool returns figi_candidates instead of asserting an answer. However, it does not provide explicit when-not-to-use guidance or name alternative tools, so the exclusion half of usage guidance is missing.

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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. Some overlap exists in the Polymarket-related tools, but each has a specific focus (arbitrage detection, edge scanning, persistence tracking, fill risk, cross-venue spread). The two ask_pipeworx variants are similar but differentiated by hallucination resistance.

Naming Consistency5/5

All tool names use lowercase with underscores, following a consistent pattern of verb_noun or noun_verb. Examples include 'ai_visibility_check', 'bet_research', 'entity_profile', and 'validate_claim'. There are no mixed conventions or erratic naming.

Tool Count2/5

The server is named 'Corporate Apology' but contains 31 tools, only one of which (corporate_apology_generate) relates to apologies. The vast majority are unrelated data retrieval and analysis tools, making the count excessive and poorly scoped for the server's stated purpose.

Completeness1/5

For a server focused on corporate apologies, the only tool is corporate_apology_generate. There are no tools for analyzing apology impact, managing crisis response, or tracking apologies. The tool surface is severely incomplete relative to the server's name.

Resources