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Glama

Blockchair

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, but the description adds substantial behavioral context beyond that: it explains cascading through multiple endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, handling of ambiguous matches (returns figi_candidates), explicit labeling of unresolved identifiers, and how ISINs resolve to legal entities. This exceeds what annotations convey and fully informs the agent of edge cases.

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 but densely informative; every sentence contributes functional detail. It is front-loaded with usage examples and the 'Use FIRST' directive. The structured layout with 'SUPPORTED TYPES' and sub-bullets aids scanning. It is not excessively verbose given the complexity, but slightly tighter organization could earn a 5.

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?

With no output schema, the description must explain return values and behavior, and it does thoroughly: it mentions figi_candidates for ambiguous matches, the unresolved array, source-labeled identifiers, and graceful degradation. It covers input handling, edge cases, and internal cascading, leaving nothing essential for an agent to invoke the tool correctly.

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 description coverage is 100%, so the baseline is 3, but the description adds critical semantic guidance not in the schema: for 'value', it clarifies that for bonds the issuer must be passed exactly as printed and warns against including security-class words (e.g., 'revenue bonds'). It also explains the input formats (ticker, CIK, ISIN, name) and how they resolve differently. This goes well beyond the schema's generic descriptions.

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 clear purpose: resolving a user-spoken name to canonical/official identifiers required by other tools. It provides multiple example queries and lists supported entity types (company, drug) with specific identifier details. It clearly distinguishes itself from sibling tools like entity_profile and compare_entities by focusing on name-to-ID resolution.

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 explicitly says 'Use FIRST whenever you have a name but need an ID' and notes it replaces 2-3 manual lookups, giving clear guidance on when to invoke it. It does not explicitly name alternative tools or exclusion conditions, but the 'FIRST' directive and the context of siblings strongly imply it is the primary resolver. A slightly stronger exclusion note would push this to 5.

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

B3.2/5.0
Disambiguation2/5

Several tight clusters of overlapping tools: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer factual questions (beta is currently identical to stable per its own description), six Polymarket tools all surface betting/edge opportunities, and ai_visibility_check vs scan_competitor_ai_presence duplicate functionality. Despite long descriptions, an agent could easily pick the wrong tool.

Naming Consistency2/5

Naming mixes bare single-word nouns (address, block, node, stats, transaction), bare verbs (remember, recall, forget, subscribe), verb_noun compounds (generate_llms_txt, scan_dependency, compare_entities), and prefixed families (polymarket_*, pipeworx_*, ask_pipeworx*). Some clusters are internally consistent, but the blockchain endpoints break the verb convention entirely and there is no uniform pattern across the set.

Tool Count2/5

36 tools spanning at least six unrelated domains — blockchain explorer, structured-data research, prediction markets, AI visibility, memory, and subscriptions — is too heavy for a coherent server. The count exceeds the 25+ threshold and reflects scope creep rather than a focused purpose.

Completeness3/5

The Pipeworx research surface is near-complete (discover/resolve/ask/ground/verify/search-within plus entity/profile/compare), prediction markets are exhaustively covered, and memory/subscriptions have full lifecycles. But the server's namesake domain — Blockchair blockchain data — is thin at just five basic queries with no fee estimation, mempool, or deeper chain analytics, leaving notable gaps in the surface implied by the server name.

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