Skip to main content
Glama

Powo

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

A4.2/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the readOnly/idempotent annotations: it discloses internal cascading across lookup endpoints, graceful degradation when GLEIF or OpenFIGI is unavailable, and the specific sources and outputs (EDGAR, GLEIF, OpenFIGI, RxNorm, pipeworx citation). This meaningfully helps an agent understand side effects and failure behavior, and does not contradict the annotations.

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 description is information-dense but poorly structured: a single long paragraph with deep parentheticals, uppercase emphasis, and many embedded clauses. It front-loads the core purpose and examples, so it is not uselessly verbose, but it could be significantly more scannable with bullets or shorter sentences.

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 compensates by specifying the returned identifiers and citations, supported entity types, accepted input formats, source coverage, and fallback behavior. It covers edge cases like non-US issuers and non-equity instruments, making the tool sufficiently complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters with descriptions, so the baseline is 3. The description goes beyond the schema by explicitly adding ISIN as an accepted company input, explaining how non-equity instruments resolve via name search, and clarifying that drug lookups accept brand or generic names. This is useful additional semantics.

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 clearly states that the tool resolves user-spoken entity names into canonical identifiers required by other tools, and enumerates supported types ('company' and 'drug') with specific identifier outputs. It positions the tool as a name-to-ID lookup but does not explicitly name sibling tools for contrast, so it is clear but not fully differentiated by sibling comparison.

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 use trigger: 'Use FIRST whenever you have a name but need an ID.' It also explains supported input forms and graceful degradation, which helps an agent decide when to call it. However, it does not mention alternatives or when not to use it, so it lacks the full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Powo toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Multiple tools appear to do nearly the same thing, notably ask_pipeworx and ask_pipeworx_beta (the description explicitly says they currently match exactly), plus ask_pipeworx_grounded and deep_research which are all variations of the same routing/query capability. The Polymarket tools and the AI-visibility tools also have overlapping boundaries, making it easy for an agent to pick the wrong one.

Naming Consistency4/5

Most tools follow a readable snake_case verb_noun pattern like ask_pipeworx, compare_entities, resolve_entity, scan_dependency, and validate_claim. There are minor deviations such as entity_profile, bet_research, pipeworx_feedback, and recent_changes, but the naming is largely predictable and clearly grouped by prefixes like polymarket_ and pipeworx_.

Tool Count2/5

With 33 tools, this server exceeds the threshold where the count becomes a burden rather than a benefit. The set covers many disparate domains—general data querying, Polymarket betting, plant taxonomy, npm auditing, AI visibility, memory, and subscriptions—so the surface feels over-scoped for a single server.

Completeness4/5

The core research/query workflow is well covered: discovery, lookup, grounded answers, deep research, entity resolution, comparison, validation, and change tracking are all present. Subscription and memory lifecycles are also complete; the main gaps are minor, such as no explicit tool for fetching a pipeworx:// citation URI directly.

Resources