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Glama

Octopus Energy

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 annotations (readOnly, openWorld, idempotent), the description reveals important runtime behavior: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, explicitly reports unresolved identifiers under 'unresolved', and returns 'figi_candidates' on ambiguity instead of guessing. This is rich, non-obvious behavioral context that materially improves agent decision-making.

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 information-rich, with the core purpose front-loaded before the supported types. It is longer than ideal and contains long parenthetical chains, but every major detail serves a real disambiguation purpose, making the length largely justified.

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 the tool's complexity and lack of an output schema, the description provides impressive coverage: it names the identifiers returned per type, explains source provenance, describes failure and degradation modes, and clarifies ambiguity handling. An agent has enough context to know what this tool will do and what to expect back.

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?

Although schema coverage is 100%, the description significantly enhances parameter understanding: it explains the 'type' enum values, gives concrete formats for 'value' (ticker, CIK, ISIN, company name, drug brand/generic), and adds crucial nuance like how ISIN maps to legal entities and how trailing security-class words break bond lookups. This goes well beyond the schema.

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 states a specific purpose: resolving a user-spoken name to canonical/official identifiers that other tools require as input. It lists concrete query examples and supported entity types, and differentiates itself from sibling tools like entity_profile by emphasizing ID lookup rather than profile retrieval.

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 instructs 'Use FIRST whenever you have a name but need an ID,' which gives clear contextual guidance. It also explains how to handle ambiguous bond names and warns against passing full noun phrases, but it does not explicitly name alternative tools for cases where an ID is already known.

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
Disambiguation4/5

Most tools have clearly distinct purposes due to detailed descriptions and specific domains. However, there is potential for confusion between `ask_pipeworx` and `ask_pipeworx_grounded` (both route to data), and among the four polymarket tools, which could cause misselection if not read carefully.

Naming Consistency2/5

Naming is inconsistent: while all use snake_case, they mix verbs (`ask_`, `list_`, `bet_`, `scan_`), noun phrases (`entity_profile`, `product_details`, `recent_alerts`), and imperative verbs (`forget`, `remember`, `recall`). No single pattern is followed throughout, making it harder to predict tool names.

Tool Count3/5

With 31 tools, the server covers multiple domains (energy, general data, betting, memory, subscriptions) which feels heavy for a single server named 'Octopus Energy'. While each tool has a role, the scope is overly broad, and many tools are tangential to energy, suggesting the number could be reduced or better scoped.

Completeness3/5

For the energy domain, tools are present but only cover listing products and tariffs, missing account management or switching. For the broader domains (data, prediction markets), coverage is decent but lacks some expected features like browsing all available data sources or user profile management. The set is not fully comprehensive for any single purpose.

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