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Glama

Soilgrids

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

A5/5.0
Behavior5/5

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

Annotations already mark it read-only, idempotent, open-world, and non-destructive. The description adds meaningful behavioral context beyond those hints: it cascades through several lookup endpoints internally, gracefully degrades when GLEIF/OpenFIGI are unavailable, returns EDGAR identifiers regardless, and reports unresolved identifiers explicitly. This gives the agent a clear mental model of the operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is long, but every sentence earns its place. It is front-loaded with purpose and usage priority, then organizes details by supported types and edge cases. Despite its density, it reads as a well-structured reference rather than a wall of text, with no filler or redundant restatements of the tool name.

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 two entity types, multiple upstream sources, and no output schema, the description is remarkably complete. It specifies the return identifiers (CIK, ticker, LEI, FIGI, RxCUI, ingredient, brand), the citation format, degradation behavior, and input constraints. An agent has enough context to invoke it correctly across the supported use cases.

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 the schema already documents both parameters at 100% coverage, the description adds substantial semantic value: concrete examples for each type, accepted formats (ticker, CIK, ISIN, name), the 'entity name only' rule, and the bond-issuer caveat about trailing security-class words. This goes well beyond the schema and prevents common misinputs.

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 examples and a clear statement: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It immediately distinguishes the tool's role from siblings like compare_entities or entity_profile by emphasizing the name-to-ID lookup function, and it further differentiates by supported entity types and identifier sources.

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?

The description explicitly instructs 'Use FIRST whenever you have a name but need an ID,' which directly guides selection. It also gives detailed input-format rules, including what to pass for tickers, CIKs, ISINs, company names, drugs, and bonds, plus a negative example ('never the question's full noun phrase'), making the invocation conditions unambiguous.

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 overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve similar question-answering roles. The soil-specific tools are distinct but mixed with many general tools, causing confusion.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use snake_case (ask_pipeworx, list_soil_properties), others are compound nouns (suggest_questions, deep_research), and there is no uniform pattern like verb_noun across the set.

Tool Count3/5

With 34 tools, the count is high but might be justified for a broad platform. However, the server name 'Soilgrids' suggests a focused soil data service, making the count feel excessive and unfocused.

Completeness3/5

For soil data, the three dedicated tools (list_soil_properties, soil_classification, soil_properties) cover basic needs but lack advanced queries. The general tools are extensive but introduce many gaps unrelated to soil, so overall completeness for the server's stated purpose is mediocre.

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