Skip to main content
Glama

Yc Rejection

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?

Annotations already mark the tool read-only/idempotent, and the description goes far beyond them by disclosing internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` output, and candidate-returning behavior on ambiguous matches. This is exactly the behavioral context an agent needs.

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 front-loaded with query examples and the primary use instruction, and the content is dense and information-rich. However, the company branch is one long run-on with deep parentheticals, and the closing 'replaces 2-3 manual lookups' is minor promotional fat.

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 carries the burden of explaining return shape, and it does: CIK/ticker/company_name, LEI plus ownership, FIGI candidates, unresolved identifiers, RxCUI/ingredient/brand, and the citation link. All necessary call guidance is present for both supported types.

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?

Despite 100% schema description coverage, the description adds substantial parameter meaning: accepted formats for `value` (ticker/CIK/ISIN/name; brand/generic), the exact-issuer rule for bonds, and what each `type` resolves to. This goes well beyond the schema's terse 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 opens with concrete user-phrase examples and states the operation as resolving a spoken name to canonical identifiers, with a crisp 'Use FIRST whenever you have a name but need an ID.' The two supported types and their identifier outputs make it easy to distinguish from sibling tools like entity_profile and 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?

Gives an explicit trigger ('Use FIRST whenever you have a name but need an ID') and concrete query paraphrases, and includes a strong negative instruction for bond lookups: pass the issuer exactly, never the full noun phrase. It doesn't explicitly name sibling alternatives or state when to prefer entity_profile/compare_entities, so it falls just short of a 5.

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

All Yc Rejection 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

B3.3/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants and several Polymarket analysis tools. The presence of meta-tools like discover_tools and suggest_questions adds confusion. Distinguishing between tools like entity_profile, compare_entities, and recent_changes requires careful reading of descriptions.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, ask_pipeworx), others use underscores (compare_entities, deep_research). Prefixes like pipeworx_ and polymarket_ are inconsistently applied, and there is no clear verb_noun pattern across the set.

Tool Count2/5

With 32 tools, the server is heavily over-scoped for its name 'Yc Rejection'. Only one tool directly relates to that domain. The rest constitute a full data platform, making the count far too high for the implied narrow purpose.

Completeness1/5

For a server named 'Yc Rejection', the tool set is severely incomplete: only one tool generates rejection text. There are no tools for application management, review, or related tasks. The actual completeness of the underlying platform is irrelevant given the misleading name.

Resources