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omniseek_resolve_identity

Resolve a person's name to ranked candidate author IDs from OpenAlex and Semantic Scholar, so you can disambiguate homonyms and map co-author relationships.

Instructions

Resolve a PERSON's name to candidate author ids — the shared front door for EVERY relationship layer (you must know WHICH person before you can map their connections).

OmniSeek's other tools keyword-search PAPERS; this resolves an AUTHOR. It NEVER silently picks — it returns ranked CANDIDATES so YOU disambiguate (the homonym trap: "Zhennan Shen" is three different people in OpenAlex). hint (e.g. an institution like "HKUST", or a field) only RE-ORDERS candidates, never filters them. source: "auto" (OpenAlex first, pulls in Semantic Scholar when the top OpenAlex hit is sparse — i.e. a likely junior / arXiv-frontier author OpenAlex hasn't indexed), "openalex", or "s2".

paper (an arXiv id / DOI / title of a KNOWN paper by this person) is the reliable way to pin a COMMON-NAME JUNIOR — it resolves straight from the paper's author list, where a bare name search fails (e.g. many distinct researchers share a common name like "Wei Zhang"; their paper fixes the exact id).

Use the returned id with omniseek_coauthors. ambiguous: true means two comparable candidates — confirm with a hint / a paper / a known co-author before trusting either.

likely_same_person (when present) groups same-name same-backend candidates that are likely ONE person SPLIT across ids, with a ready-to-paste merge_token ("A123+A456") you can hand straight to omniseek_coauthors as one input; it never auto-merges, just surfaces the candidate merge.

Returns: {query, source, candidates:[{id, source, name, works_count, cited_by, institution, via_paper?}], ambiguous, note, likely_same_person?:[{source, ids, name, merge_token, note}], degraded?:{openalex}}. degraded (when present) means the OpenAlex lookup FAILED (rate-limited / upstream down): an empty/thin result is then missing-data, NOT a confirmed "not in the graph" — retry, or pass source='s2' / paper=.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNo
nameYes
paperNo
sourceNoauto

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It states the tool NEVER silently picks, hint only re-orders and never filters, likely_same_person never auto-merges but surfaces a merge_token, and degraded indicates upstream failure rather than confirmed absence. These details go well beyond the minimum and directly prevent misinterpretation.

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 appropriately sized for a complex disambiguation tool with four meaningful parameters, no annotations, and no output schema. It front-loads the core purpose and key safety behavior, then logically builds through parameter semantics, disambiguation flags, and failure semantics. Almost every sentence carries operational 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?

Since there is no output schema, the description documents the full return shape and explains each important flag (ambiguous, likely_same_person, degraded) with actionable consequences. It also connects the workflow to omniseek_coauthors and gives retry guidance for degraded upstream failures, making the definition self-sufficient.

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 0%, so the description must compensate for all parameters. It adds exact meanings for hint (re-order only, never filters, example 'HKUST'), source ('auto', 'openalex', 's2' with fallback behavior), paper (arXiv id/DOI/title, resolves straight from the author list), and the required name (a PERSON's name). This is substantially richer than the bare input 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 leads with a specific verb and resource ('Resolve a PERSON's name to candidate author ids') and immediately distinguishes it from sibling tools: other OmniSeek tools keyword-search PAPERS, while this resolves an AUTHOR. It also states the workflow position as the shared front door for relationship layers, so an agent can clearly tell why this tool exists.

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 contrasts this tool with paper-searching tools and instructs agents to use the returned id with omniseek_coauthors. It gives concrete selection guidance for the 'paper' parameter (common-name junior authors), explains the source options and their behaviors, and tells the agent how to handle ambiguous results.

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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