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omniseek_coauthors

Map co-authorship networks from author names or IDs to reveal advisors, top collaborators, pairwise connections, bridges, and sub-communities from joint-paper counts.

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.

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. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and meets it: it discloses that co-authorship is reconstructed from PRIOR work, split IDs are merged, results are mechanical and non-interpretive, sub-community cooc is included, and 'degraded' means upstream lookup failure. This is far beyond a typical description.

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 and dense, but it is front-loaded with the core use case and uses bullets and a structured return summary. Some repetition exists (the prior-work reconstruction is mentioned twice), yet nearly every sentence carries operational value for a tool with no annotations.

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?

There is no output schema, so the description must explain return values and edge cases. It provides the full return shape, semantics for nodes/edges/bridges/cooc, the degraded flag meaning, and instructs how to harvest ids for further drill-down. For the tool's complexity, this is complete.

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%, but the description compensates fully. It explains author inputs as names, ids, or '+'-joined ids for merged split identities; hints/papers as parallel disambiguation lists; and source via the openalex default and the 's2' alternative for juniors. Each parameter gains operational meaning that the schema lacks.

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 concrete use case: 'WHO a researcher collaborates with — advisor + closest collaborators by joint-paper count, or how a paper's author group is connected.' This clearly states the verb, resource, and scope. It also distinguishes itself from WebSearch and names the sibling tool for the citation/influence layer, so an agent can tell it apart immediately.

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 'Use WHEN' conditions, explains the N=1 vs N>1 modes, tells the agent to pull names/ids from omniseek_resolve_identity, and explicitly routes to omniseek_field_skeleton for a different layer. It also tells the agent that an empty graph may be missing data to retry, not a real 'no collaborators' result.

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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