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Microsoft Todo

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses graceful degradation when GLEIF/OpenFIGI is unavailable, multi-endpoint cascading, non-assertion with figi_candidates on ambiguous matches, and explicit reporting of unresolved identifiers. It also explains that multiple sources are stitched together, which is behavior the annotations alone cannot 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 front-loaded with examples and the primary usage rule, and every major sentence adds operational value. However, the company-type section is an extremely dense single parenthetical, and the overall length is high even for a complex tool.

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 there is no output schema, the description carries the burden of explaining return behavior, and it does: it names figi_candidates, unresolved, source labels, and drug return fields. It also covers edge cases like non-US issuers, ambiguous instrument names, and degraded enrichment, making it complete enough for reliable agent invocation.

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?

Though the schema already covers both parameters, the description adds essential nuances: the ENTITY-NAME-ONLY rule, the exact-issuer requirement for bonds, trailing security-class words matching nothing, ISIN-to-LEI resolution, and how non-ticker instruments still resolve. These details materially improve the likelihood of a correct first call.

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-phrase examples and states the exact job: resolving a user-spoken NAME to canonical/official identifiers other tools require. It also enumerates supported entity types and differentiates itself by acting as the entry point for identifier lookups, making it clearly distinct from siblings like entity_profile or validate_claim.

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 gives the agent a clear triggering condition. It does not name specific sibling alternatives or state when not to use this tool, so it stops short of a full when/when-not contrast.

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

B3.4/5.0
Disambiguation1/5

The set is dominated by overlapping Pipeworx and prediction-market tools, with ask_pipeworx and ask_pipeworx_beta explicitly described as currently identical, and ask_pipeworx_grounded, deep_research, and discover_tools serving heavily overlapping lookup purposes. The four Microsoft To Do tools are buried among 31 unrelated tools, making it very hard for an agent to select the right tool for the server's apparent domain.

Naming Consistency2/5

Most names are snake_case, but the naming conventions are otherwise mixed: there are verb_noun tools like list_tasks and get_task, bare verbs like remember/forget/recall, prefixed families like polymarket_*, and noun-phrase names like entity_profile, recent_changes, and pipeworx_trending. The lack of a consistent pattern across the set, especially relative to the Microsoft To Do server name, makes naming unpredictable.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them are for Microsoft To Do, the server's stated name and purpose. The remaining 31 tools belong to unrelated Pipeworx data, prediction-market, and memory-management domains, which is an extreme scope mismatch.

Completeness1/5

For a Microsoft To Do server, the surface is severely incomplete: list_task_lists, list_tasks, get_task, and find_due_tasks cover reading and browsing only, with no create, update, complete, or delete operations. Even the broader Pipeworx functionality is scattered and redundant rather than forming a coherent domain surface.

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