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Google_sheets

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.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses important behavioral traits: internal cascading through multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguity behavior with `figi_candidates`, explicit `unresolved` reporting, and source labeling of identifiers. This is substantial added transparency beyond what annotations provide.

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 it earns its length by covering two entity types, input variations, edge cases, and fallback behavior. It is front-loaded with user-facing query examples and the 'Use FIRST' guidance. Some parentheticals are dense and slightly repetitive, but overall the structure is organized and each section adds value.

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 there is no output schema, the description does an excellent job of explaining what the caller should expect: canonical identifiers with source labels, unresolved identifiers, figi_candidates on ambiguity, and degraded enrichment. It covers both supported types and important edge cases like ISIN resolution and bond issuer names. An agent has enough context to invoke and interpret results 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?

Although schema description coverage is 100%, the description adds critical meaning beyond the schema: accepted input formats (ticker, CIK, ISIN, name), the 'entity name only' rule with a concrete example of what not to pass, and clarification of type-specific behavior (e.g., ISIN resolves to the legal entity via GLEIF). This goes well beyond the enum and string descriptions in 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 precise verb and resource: resolve a user-spoken name to canonical/official identifiers. It gives concrete query examples, enumerates the supported entity types, and clarifies the distinction between entities the tool can resolve and those it cannot (non-equity instruments) — so an agent can tell it apart from lookup tools like entity_profile without opening the schema.

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', which is strong when-to-use guidance. It also provides practical input rules (pass the issuer exactly as printed, not the full noun phrase) and mentions that it replaces 2-3 manual lookups. It does not mention explicit exclusions or sibling alternatives, but the usage context is clear.

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

B3.1/5.0
Disambiguation2/5

Several tools are nearly identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same, and ask_pipeworx, ask_pipeworx_grounded, and deep_research all serve overlapping research/query purposes. discover_tools and suggest_questions both act as discovery entry points, while the five polymarket tools differentiate primarily through intricate details that are easy to confuse.

Naming Consistency2/5

Naming conventions are mixed across the set: verb_noun (ask_pipeworx, resolve_entity, validate_claim), noun_verb (sheets_append, sheets_create), noun_noun (polymarket_arbitrage, entity_profile), and bare verbs (forget, recall, subscribe). The sheets tools are internally consistent but the broader collection has no uniform pattern, with awkward names like pipeworx_trending and search_within.

Tool Count2/5

With 36 tools, the server exceeds the 25-tool threshold for 'too many'. The Google Sheets portion accounts for only 5 tools, while the majority are highly specialized Pipeworx and Polymarket tools that could be consolidated or dramatically reduced. The count feels inflated relative to the advertised 'Google_sheets' server name and its actual core purpose.

Completeness2/5

For a server named Google_sheets, the essential operations exist (create, read, write, append, list structure), but there is no delete/clear range tool and no way to add a new sheet to an existing spreadsheet. For the broader data/research scope, important lifecycles are missing (e.g., no direct way to write Pipeworx results into Sheets, no update for subscriptions, only cancel). The set falls short of fully covering either domain.

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