Skip to main content
Glama

Openchargemap

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?

Even though annotations already mark the tool read-only and idempotent, the description adds substantial behavioral context: it cascades through multiple lookup endpoints, degrades gracefully if LEI/FIGI sources are unavailable, explicitly marks unresolved identifiers, and returns candidates rather than asserting matches for ambiguous names. There is no contradiction with 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.

Conciseness4/5

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

The description is long and dense, but well structured: core purpose first, then per-type details, then failure behavior. Every section delivers useful operational detail. It loses a point for verbosity and some redundancy in the company-type paragraph, but the organization makes it navigable.

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 the absence of an output schema, the description is remarkably complete. It covers accepted input forms, output labeling with sources, unresolved-identifier behavior, ambiguity handling, enrichment limitations, and even the underlying rationale for returning candidates. An agent has enough context to know when and how to invoke it correctly.

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?

Schema coverage is 100%, so a baseline of 3 applies. The description adds meaningful semantic depth beyond the schema by warning to pass the entity name only, clarifying that bond issuers must be passed exactly as printed, and explaining that trailing security-class words will fail to match. This is useful extra guidance, though the schema already lays out the basic parameter meanings.

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 a concrete, actionable verb: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates the supported types (company, drug) and the identifier families (CIK, LEI, FIGI, RxCUI), and explicitly positions itself as the first step when an ID is needed. This clearly distinguishes it from sibling tools like entity_profile or 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 explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains when results are intentionally not asserted, such as ambiguous FIGI matches. However, it does not explicitly name when-not-to-use alternatives or what to use instead when the goal is deeper entity research rather than identifier resolution.

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

All Openchargemap 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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical; Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) are hard to distinguish; memory tools (remember, recall, forget) and subscription tools (subscribe, unsubscribe, list_subscriptions, recent_alerts) also create ambiguity.

Naming Consistency2/5

Tool names mix snake_case (find_stations, get_station) with inconsistent verbs and noun phrases (ai_visibility_check, entity_profile, validate_claim). No clear pattern emerges, and some names are verbose or unclear (e.g., scan_competitor_ai_presence).

Tool Count2/5

At 33 tools, the set is too large for a focused server. Many tools are unrelated to the server name 'Openchargemap' (EV charging), and the collection feels like a grab bag of unrelated functionalities (Pipeworx data, Polymarket betting, AI visibility, memory management).

Completeness2/5

For the implied domain of EV charging, only two tools exist (find_stations, get_station), leaving major gaps (no CRUD). As a general utility, it lacks coverage in many areas (e.g., no file handling, no scheduling). The set is incomplete both as a domain-specific and general-purpose server.

Resources