Skip to main content
Glama

uniprot-mcp-server

by io.github.cyanheads

uniprot-mcp-server: get proteome

uniprot_get_proteome
Read-onlyIdempotent

Fetch the reference proteome for an organism by UPID (e.g. "UP000005640") or NCBI taxon ID (e.g. 9606) — provide exactly one. Returns metadata inline: proteome type, total protein count, BUSCO completeness (score, complete/fragmented/missing counts, lineage dataset), and the genome assembly accession. The protein set is opt-in via include_proteins (it is large — human is ~147,506) and returns a capped page with a forward cursor; narrow it with the query filter (UniProtKB Lucene syntax) for a subset. Resolve an organism name to a taxon ID first with uniprot_get_taxonomy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoProteins per page when include_proteins is true (max 500). Omit for the server default.
upidNoProteome UPID. Provide this OR taxon_id, not both.
queryNoOptional UniProtKB Lucene filter to narrow the protein list, e.g. "reviewed:true AND keyword:KW-0067". Only applies when include_proteins is true.
cursorNoForward-pagination cursor from a prior protein page. Only meaningful with include_proteins.
taxon_idNoNCBI taxon ID, e.g. 9606 for human. Resolves to the reference proteome. Provide this OR upid, not both.
include_proteinsNoWhen true, also return a capped, cursor-paginated page of the proteome's proteins. Defaults to false — metadata alone is the common case.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
capNoThe page-size cap that was applied.
errorNoPresent when the call failed. Absent on success.
shownNoNumber of proteins returned in this page.
cursorNoForward cursor for the next protein page. Absent on the last page.
noticeNoTruncation guidance when the protein page was capped — how to reach the rest (walk the cursor or narrow with the query filter).
proteinsNoA capped page of the proteome's proteins. Present only when include_proteins is true.
proteomeNoProteome metadata.
truncatedNoTrue when the protein page hit the size cap — more remain via cursor.
totalProteinsMatchedNoTotal proteins matching the (optionally filtered) proteome query.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses concrete behavior: the returned metadata fields, large protein set size (~147,506 for human), capped pages with a forward cursor, and optional query filtering. This gives the agent strong expectations about cost and paging behavior.

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 dense but efficient, with no filler. Each sentence carries distinct value: what the tool fetches, what metadata comes back, how proteins are paginated and filtered, and what to call first for organism names.

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 tool with 6 parameters, an output schema, and sibling tools, the description is complete: it covers input selection rules, output highlights, pagination, size caveats, query behavior, and the relevant prerequisite workflow. The output schema covers the full return shape, so the description does not need to repeat it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has 100% parameter coverage, so the baseline is 3. The description adds useful context beyond schema details: the protein list is very large, pagination is capped, and the query filter applies specifically to the protein subset. It reinforces the exclusive upid/taxon_id relationship though the schema already states it.

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 starts with a specific verb and resource: 'Fetch the reference proteome for an organism by UPID... or NCBI taxon ID.' It clearly distinguishes this from sibling tools by focusing on proteome-level retrieval rather than individual entries, sequences, or ID mapping.

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 usage context: provide exactly one of upid or taxon_id, metadata-only is the common case, proteins are opt-in via include_proteins, and query can be narrowed with a Lucene filter. It also names the prerequisite tool, uniprot_get_taxonomy, for resolving organism names.

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

All uniprot-mcp-server 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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: retrieving entries, proteomes, sequences, taxonomy, mapping IDs, and searching proteins. There is no overlap; even the batch entry retrieval is distinct from the sequence-only retrieval.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with a consistent prefix: uniprot_get_entry, uniprot_get_proteome, uniprot_get_sequence, uniprot_get_taxonomy, uniprot_map_ids, uniprot_search_proteins. This makes it easy to predict available operations.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose of querying and retrieving UniProt data. Each tool serves a core function and no tool feels redundant.

Completeness4/5

The toolset covers the main workflows: search proteins, fetch full entries, fetch sequences, map IDs, resolve taxonomy, and fetch proteomes. Minor gaps include lack of tools for browsing taxonomy children inline or fetching isoforms in entries, but these can be worked around with existing tools.

Resources