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Statuspage

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

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, it discloses source-labelled identifiers, unresolved entries being explicitly placed under `unresolved`, graceful degradation when GLEIF or OpenFIGI is unavailable, and the internal cascade that replaces 2–3 manual lookups. These are non-obvious behaviors an agent could not infer from annotations or schema alone.

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

Conciseness3/5

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

The core use case and 'Use FIRST' rule are front-loaded, and nearly every clause adds useful information, but the body is one extremely dense, parenthetical-heavy paragraph. A bulleted or sectioned structure would make the company/drug behaviors and edge cases far more scannable.

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?

For a two-parameter tool with no output schema, the description covers input constraints, output identifier families, fallback behavior, and non-US/corporate-bond edge cases. An agent has enough information to select it and call it correctly, including knowing what will and will not resolve.

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?

Schema coverage is 100%, so baseline is 3, but the description meaningfully extends the schema: `value` is detailed with accepted formats (ticker, CIK, ISIN, company name), drug brand/generic examples, and the exact-as-printed issuer guidance for bonds, including which trailing words should be omitted. This materially reduces the chance of misparsing user intent.

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 leads with concrete user phrasings and states a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates the supported types (company/drug) and the identifiers returned, and 'Use FIRST whenever you have a name but need an ID' positions it clearly within the sibling toolset.

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 gives an explicit trigger condition—'Use FIRST whenever you have a name but need an ID'—and reinforces what inputs are accepted. However, it does not name alternative tools or explicitly say when not to use it, such as when an existing entity profile rather than identifier resolution is needed.

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
Disambiguation2/5

Multiple tools serve the same 'ask a question' function (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish. Entity-oriented tools like entity_profile, recent_changes, and compare_entities also overlap on company information, making selection ambiguous.

Naming Consistency4/5

All tool names are lowercase snake_case and mostly descriptive, with consistent prefixes for tool families (ask_pipeworx, polymarket_*, statuspage_*). However, there is no uniform verb_noun pattern — some are bare verbs (forget, remember, recall) while others are noun phrases (entity_profile, recent_changes) — so predictability is moderate.

Tool Count2/5

With 35 tools, this set is heavily overloaded for a server named Statuspage; only 4 tools actually relate to status pages, while the rest form a broad data query, prediction market, and memory toolkit. The count is far beyond what the name implies and dilutes focus.

Completeness2/5

For a Statuspage server, the tool surface is severely incomplete: it only reads status and incidents and provides no way to create, update, or resolve incidents. Even for the broader data-tool domain, there are gaps — no direct record-fetch by ID and no write/update operations for data sources.

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