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Glama

Shodan

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?

Even with readOnlyHint, idempotentHint, and openWorldHint already present, the description adds substantial non-obvious behavior: graceful degradation when GLEIF/OpenFIGI are unavailable, explicit reporting of unresolved identifiers, returning figi_candidates instead of asserting on ambiguous matches, and cascading through multiple lookup endpoints.

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 nearly every sentence carries operational value, and the core purpose is front-loaded with query examples. The structure is more run-on than modular, with several deep parentheticals, but it remains scannable for an agent given the 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?

For a tool with no output schema, the description thoroughly covers what will happen: which identifiers return, how ambiguous matches are surfaced, how failures are reported, and what degrades. An agent has enough context to know when to call it 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?

Schema coverage is 100%, but the description adds critical semantics beyond it: the value parameter accepts ticker, CIK, ISIN, or name for company; the ISIN-to-LEI behavior is explained; and the warning to pass only the entity name and not the full noun phrase prevents a likely misuse. This materially improves correct invocation.

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 pairing: "resolve a user-spoken NAME to the canonical/official identifiers other tools require as input." It goes further by listing concrete query phrasings and naming the supported entity types (company, drug), which clearly distinguishes it from siblings 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?

The description gives an explicit trigger: "Use FIRST whenever you have a name but need an ID." It also gives many example queries and explains that it replaces 2-3 manual lookups. It does not explicitly name alternatives or state when not to use it, but the usage context is clear enough.

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

Several tool families have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, ask_pipeworx_grounded and validate_claim both verify claims against sources, and polymarket_edges/polymarket_arbitrage/bet_research all surface prediction-market opportunities. Agents would need to read very long descriptions to distinguish overlapping intents, and would likely misroute requests.

Naming Consistency3/5

All names use snake_case, which is a consistent base, and subgroups (shodan_host*, ask_pipeworx*, polymarket_*) follow internal patterns. However, conventions mix verb-first (ask_pipeworx, validate_claim, resolve_entity) with noun-first (entity_profile, bet_research, recent_alerts), and parallel functionality is named with inverted ordering (ai_visibility_check vs scan_competitor_ai_presence).

Tool Count2/5

34 tools is above the comfortable range for a single server and the breadth feels bloated, especially with five highly niche Polymarket tools. More critically, the server is named 'Shodan' but only 3 of 34 tools are Shodan-related, so the count is badly mismatched with the stated identity.

Completeness2/5

For a server claiming to be Shodan, the surface is severely incomplete — only host lookup, search, and count are present, with no DNS, alerting, or other standard Shodan operations. For the actual Pipeworx/Polymarket domain implied by most tools, coverage is fuller but still has gaps such as no direct tool to fetch a pipeworx:// citation URI, and the memory/subscription features feel bolted on rather than integral.

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