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Glama

Cloudflare Radar

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/openWorld/idempotent annotations, the description discloses ambiguity handling (returns figi_candidates and asserts nothing when multiple instruments match), explicit unresolved fields, graceful degradation if GLEIF/OpenFIGI is unavailable, and internal multi-endpoint cascading. This is exactly the behavioral context the annotations don't carry.

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 front-loaded with the core purpose, but the opening example list and long parenthetical about instrument matching make it longer than strictly necessary. Still, most sentences earn their place given the tool's multi-backend 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 no output schema, the description still explains what comes back: canonical IDs with source labels, `unresolved` for failures, `figi_candidates` for ambiguity, and legal-entity resolution. It covers both supported types and failure modes, so an agent has enough to invoke it 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?

Even though schema coverage is 100%, the description adds critical semantics: value must be the entity name only, trailing security-class words will fail bond matches, ISIN inputs resolve to legal entities through GLEIF, and type-specific accepted forms. This substantially extends the schema's terse enum/description.

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 uses a specific verb-resource pair — 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input' — and differentiates from siblings by framing it as the first-step lookup tool. It covers supported types and gives concrete question examples, so an agent can select it over entity_profile/compare_entities.

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: 'Use FIRST whenever you have a name but need an ID.' It also explains when enrichment is unnecessary and that it replaces multiple manual lookups, but it never names an alternative tool to use when this one is not appropriate, so it stops short of full when-not guidance.

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

There are three ask_pipeworx variants that overlap heavily, plus five polymarket_* tools scanning similar market edges, and meta-tools like discover_tools, suggest_questions, and deep_research that blur together. ai_visibility_check and scan_competitor_ai_presence also overlap. The Cloudflare Radar tools are distinct, but they are a small minority in a sea of overlapping data/prediction-market tools.

Naming Consistency3/5

Most tools use snake_case, but the pattern is inconsistent: some are verb_noun (list_subscriptions, resolve_entity, scan_dependency), some are noun_phrase (bgp_leaks, internet_quality, radar_domain_rank), and some use vendor-prefixed naming inconsistently (ask_pipeworx vs pipeworx_feedback vs pipeworx_trending). It is readable but not predictable.

Tool Count2/5

37 tools is heavy for any single server, and the collection spans unrelated domains: Cloudflare Radar, Pipeworx data lookup, Polymarket betting, memory, subscriptions, and npm scanning. The count feels like a bundled platform rather than a focused tool set, and many tools could be split into separate servers.

Completeness3/5

The Pipeworx data-research side is quite complete (ask, grounded, deep_research, entity_profile, compare_entities, validate_claim, resolve_entity, discover_tools, recent_changes), and the prediction-market side has good coverage (edges, arbitrage, fill risk, cross-venue spread, tracking). However, the Cloudflare Radar portion is thin—only six tools cover a service known for many more traffic/attack/outage metrics—and the overall surface has no cohesive domain to judge completeness against.

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