scienX

find_protocol

Read-only

Protein-level protocol aggregates for a protein symbol or alias: how many protocols scienX lists, how many are curated, how many PDB structures, and the best tier. No per-record rows, no DOIs, no method text. Full methods need a free scienX sign-in.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "protein": "<protein>"
    },
    "name": "find_protocol"
  }
}

Input Schema

Name	Required	Description	Default
`protein`	Yes	Protein symbol or alias, e.g. KRAS

Output Schema

Name	Required	Description	Default
`note`	No
`n_pdb`	Yes
`aliases`	No
`protein`	Yes
`best_tier`	Yes
`full_name`	No
`n_curated`	Yes
`n_protocols`	Yes
`tier_counts`	Yes
`protocols_url`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, but the description adds real value beyond them: the aggregate-only granularity, the absence of DOIs and method text, and the fact that full methods require a free scienX sign-in, which is an authentication prerequisite an agent must know before promising detail. Only minor gaps remain, such as behavior on unknown or ambiguous symbols.

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?

Three compact sentences with the scope and return contents front-loaded, followed by the exclusions and the auth caveat. There is no filler, and each sentence adds a distinct constraint.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-shape explanation is unnecessary, and for a one-parameter read-only aggregate lookup the description covers scope, granularity, and auth limits. It falls just short of complete because it does not say how the tool behaves for proteins with no listed protocols or ambiguous aliases.

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

Parameters3/5

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

Schema description coverage is 100% and the single parameter already documents 'Protein symbol or alias, e.g. KRAS'. The description restates the same concept ('a protein symbol or alias') without adding format, normalization, or ambiguity-handling guidance, so schema coverage alone carries the load.

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 names a specific resource (protein-level protocol aggregates) for a specific input (protein symbol or alias) and enumerates exactly what comes back: protocol count, curated count, PDB structure count, and best tier. It goes further by stating what is NOT returned (per-record rows, DOIs, method text), so an agent can tell instantly whether this tool answers its question.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The negative scoping ('No per-record rows, no DOIs, no method text') implicitly tells the agent when not to use it, and the sign-in note signals where richer data lives. However, it never names an alternative tool or states a positive condition like 'use when you need summary counts across many proteins', leaving the agent to infer the routing.

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

All scienX tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?