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omniseek_coauthors

Identify a researcher's closest collaborators and advisor from joint-paper counts, or map how a paper's author list is connected through prior co-authorships.

Instructions

Use WHEN you want WHO a researcher collaborates with — advisor + closest collaborators by joint-paper count, or how a paper's author group is connected (WebSearch cannot build this). One LAYER, not the whole graph — co-authorship is one edge type; YOU overlay the others (advising, institution cohort, citation, code, social) and judge what each connection MEANS.

Fully-qualified MCP name: mcp__omniseek__omniseek_coauthors (server name is omniseek; there is no omniseek-eye server).

Pass author NAMES and/or ids (from omniseek_resolve_identity). A brand-new arXiv paper is not in the graph yet, so this reconstructs from each author's PRIOR work: • N=1 -> that author's frequency-ranked coauthor neighborhood. The advisor + closest collaborators surface by joint-paper count (e.g. Yi R. Fung -> Heng Ji ~51x = her PhD advisor, no advisor field needed — YOU read that signal). • N>1 (e.g. a paper's whole author list) -> additionally the PAIRWISE prior joint-work edges among them (with the actual joint paper titles as evidence) + BRIDGE collaborators (people who co-authored with >=2 of the inputs but are not in the set). This is the "how is this author group actually connected" reconstruction.

Each input may be a NAME, an id, or '+'-joined ids ("id1+id2") for ONE person SPLIT across ids — their works are MERGED (OpenAlex/S2 routinely split a junior's recent papers; merging recovers the complete network). Each becomes a node with resolved, ambiguous + alternatives (juniors often need source="s2", a paper anchor, or an explicit id — the node note says so when unresolved). The output also carries cooc: which of the network's top external coauthors co-appear on the same papers, i.e. the SUB-COMMUNITY structure (an ego's distinct 'research worlds'). Mechanical throughout: "these two share these N papers" is a fact; advisor-vs-peer, what a cluster MEANS, is YOUR judgment. For the citation/influence layer use omniseek_field_skeleton; for the others, assemble from the dossier recipe (github, bluesky, exa, cdp_fulltext, omniseek_read).

hints / papers are parallel lists for per-author disambiguation (an institution hint, or a known paper that pins a common-name junior).

Returns: {source, n_authors, nodes:[{query, resolved, ambiguous, alternatives, works_seen, top_coauthors:[{id,name,joint}], degraded?}], edges:[{a,b,joint_count,papers:[{title,year,id}]}], bridges:[{id,name,shared_by,total_joint}], cooc:[{a,b,n}], degraded?}. (top_coauthors/bridges carry a representative id you can harvest and pass back to omniseek_coauthors to drill that person.) A top-level/node degraded means that author's OpenAlex lookup FAILED (rate-limited / upstream down): an empty graph is then missing-data to RETRY, not "no collaborators".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintsNo
papersNo
sourceNoopenalex
authorsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A5/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure, and it delivers extensively. It explains that the tool reconstructs from PRIOR work, merges '+'-joined ids for split author profiles, reports ambiguity and alternatives, and includes a 'degraded' flag meaning the graph is missing data and should be retried. It also clarifies what is mechanical fact versus the agent's judgment.

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 dense, front-loaded with purpose and usage modes before detailing disambiguation and output structure. Every sentence earns its place given the 0% schema coverage, no output schema, and the tool's complex graph semantics. The use of bullets and a return-shape block keeps the length navigable.

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 complex graph-returning tool with no output schema and minimal input schema, the description is remarkably complete: input formats, disambiguation mechanisms, N=1 vs N>1 behavior, bridge collaborators, cooc sub-communities, output fields, failure semantics, retry guidance, and drill-down instruction. An agent has everything needed to invoke and interpret 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 0%, so the description fully compensates for all four parameters. It defines 'authors' as names, ids, or '+'-joined ids from omniseek_resolve_identity; 'hints'/'papers' as parallel per-author disambiguation lists; and 'source' with concrete guidance like source='s2' for juniors. This adds substantial meaning beyond the bare 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 opens with a specific, actionable purpose: 'Use WHEN you want WHO a researcher collaborates with — advisor + closest collaborators by joint-paper count, or how a paper's author group is connected.' It clearly distinguishes the tool from WebSearch and sibling omniseek_field_skeleton, and positions it as 'one LAYER, not the whole graph.' An agent can immediately tell what resource and relationship this tool operates on.

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 gives explicit when-to-use guidance: co-authorship exploration, advisor discovery, and author-group connectivity. It also states explicitly what NOT to use it for: 'For the citation/influence layer use omniseek_field_skeleton; for the others, assemble from the dossier recipe.' It even distinguishes N=1 vs N>1 cases and references omniseek_resolve_identity for ID inputs.

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