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Glama

Outlook Mail

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. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds substantial behavioral detail: graceful degradation ('if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return'), explicit `unresolved` list for failed lookups, and `figi_candidates` for ambiguous matches. It also discloses that each call cascades through multiple lookup endpoints. This goes well beyond the annotations and equips the agent with failure-mode expectations.

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

Conciseness5/5

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

Though long, the description is tightly organized: it front-loads the trigger with query examples, then breaks down supported types with sub-details, and ends with a practical note about internal cascading. Each sentence carries operational weight—there is no fluff or repetition. The length is justified by the tool's multi-source, multi-type 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 2 parameters, no output schema, and internal multi-endpoint logic, the description covers everything an agent needs: input semantics per type, ambiguous-match behavior (figi_candidates), unresolved handling, source labelling, and fallback degradation. It even addresses non-US issuers and non-equity instruments. The only absent detail is exact return structure, but the key fields are mentioned, so the description is sufficient.

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 descriptions are brief but the tool description massively expands on the `value` parameter: it provides concrete examples (AAPL, 0000320193, 'ozempic'), differentiates company vs drug formats, and adds the critical warning about bond issuer names versus full noun phrases. This is far beyond the schema's (already 100% covered) description, meaning the tool description is what makes correct usage possible.

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 query examples ('What's the ticker for...', 'find the CIK for...'), states the verb-resource pair ('resolve a user-spoken NAME to the canonical/official identifiers'), and explicitly says 'Use FIRST whenever you have a name but need an ID,' distinguishing it from sibling tools like entity_profile and compare_entities. The supported types (company, drug) are enumerated with sources, making the tool's scope unambiguous.

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?

The description gives an explicit trigger condition ('Use FIRST whenever you have a name but need an ID') and provides per-type input instructions, including the critical bond nuance ('pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed... never the question's full noun phrase'). It also notes that using it replaces 2-3 manual lookups, communicating efficiency. No exclusion conditions are given, but the trigger is clear enough.

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

The set contains multiple near-duplicate tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded overlap heavily (beta is explicitly identical right now), and discover_tools vs suggest_questions both serve a 'what can I do' purpose. The server name 'Outlook Mail' also misleads since only 5 of 36 tools are email-related, creating domain confusion.

Naming Consistency4/5

Most tools follow a readable snake_case verb_noun or prefixed pattern (outlook_list_messages, polymarket_edges, ask_pipeworx). Minor deviations like ai_visibility_check (instead of check_ai_visibility) and pipeworx_feedback/pipeworx_trending (noun-first) are present but don't seriously obscure meaning.

Tool Count2/5

36 tools is well beyond a typical well-scoped set, and the vast majority belong to unrelated Pipeworx/Polymarket domains while the server claims to be Outlook Mail. The count is padded by redundant variants (ask_pipeworx_beta, multiple polymarket scanning tools) that could be consolidated.

Completeness2/5

For the stated Outlook Mail purpose, the surface is read-only: you can list, search, and get messages/profile/folders, but there is no send, reply, delete, move, or mark-as-read capability—an obvious dead end for email workflows. The broader data-research side is more extensive but still lacks update paths for subscriptions/memories.

Resources