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Harrypotter

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

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses substantial runtime behavior: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF or OpenFIGI are down, deliberately resolves non-equity instruments with no ticker, returns 'figi_candidates' instead of asserting an ambiguous match, labels every identifier with its source, and states unresolved fields explicitly rather than omitting them. This provides deep insight into what happens during a call and how results should be interpreted.

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 front-loaded with a clear purpose, but it is an extremely long run-on block of text with the company case packed into a dense parenthetical. Every detail is relevant, but the lack of paragraph structure or separation between the different supported types makes it harder to parse than needed. It could be condensed and better organized without losing value.

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 tool with no output schema, the description thoroughly describes both accepted inputs and expected outputs, listing the returned identifiers from each source, the ownership hierarchy information from GLEIF when available, and the 'unresolved' section for failed resolutions. It also addresses edge cases such as ambiguous instrument names, ISIN-to-LEI mapping for non-US issuers, and partial failure behavior, making the tool fully operable from the description alone.

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 with 100% schema coverage, the description adds important meaning beyond the raw field description. It reveals that the value parameter for companies can be a ticker, CIK, ISIN, or name, and clarifies that for names of debt instruments, the value must be the issuer exactly as printed, not the full noun phrase. It also explains how the resolution behaves when a name matches multiple instruments, giving the agent practical guidance for constructing valid input.

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 example queries and immediately states its core function: resolve a user-spoken name to canonical identifiers. It goes on to specify the two supported types, the sources (SEC EDGAR, GLEIF, OpenFIGI, RxNorm), and the exact identifiers returned (CIK, LEI, FIGI, RxCUI). It distinguishes itself implicitly from sibling research tools by positioning this tool as the prerequisite lookup that supplies the IDs those tools need as input.

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?

Explicit instruction 'Use FIRST whenever you have a name but need an ID' gives a clear when-to-use rule. The description also details what to do for company versus drug lookups, what input forms are accepted (ticker, CIK, ISIN, name, brand/generic), and how to handle ambiguity with a real-world example about bond issuer names. It clarifies that if you already have an ID you likely need a different tool, thus providing exclusions.

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

A3.7/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in data-query functionality. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) also have significant conceptual overlap.

Naming Consistency3/5

All names use snake_case, so there's no camelCase mixing, but conventions vary widely: some are verb_noun (list_characters, resolve_entity, validate_claim), some are noun phrases (entity_profile, recent_alerts, polymarket_edges), and some use a product prefix (ask_pipeworx, pipeworx_feedback). The Harry Potter subset (list_characters, list_spells, list_staff, list_students) is consistent, but the overall set lacks a unified pattern.

Tool Count2/5

35 tools is well above the 25+ threshold that feels heavy, and the server's stated name suggests a narrow Harry Potter scope, yet the vast majority of tools are unrelated Pipeworx data utilities. The count appears bloated and misaligned with the apparent purpose.

Completeness2/5

For a Harry Potter server, the four listing tools are thin (no detail lookups, no filtering by ID, no sort or random), leaving obvious gaps. For a Pipeworx data server, the set is broad but still lacks obvious additions like a generic list-sources tool. The mismatch between name and content makes it impossible to call the surface complete for any single purpose.

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