Skip to main content
Glama

ChIP-seq peak calling / ChIP-seq Peak calling

chip_peak_calling
Read-only

ChIP-seq peak calling / ChIP-seq Peak calling | USE WHEN: call peaks from ChIP-seq reads | ALIASES: ChIP-seq, peak calling, 峰识别, 转录因子结合, ChIP | RETURNS: peak 清单

Input Schema

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象(不是字符串)。矩阵形式:{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]};组学结果另可带 {"groups": ["组A","组A","组B"...]}(与 cols 一一对应)、{"p_values": [...], "log2fc": [...]}(若已有差异结果)。实测缺此字段会被拒:Field required: data_json

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description only adds "RETURNS: peak 清单" (a peak list), without saying what a peak record contains, whether thresholds or genome build matter, or what happens with an ill-formed matrix.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The pipe-delimited tag format front-loads the purpose and the USE WHEN trigger well. However, the first segment duplicates the title bilingually ("ChIP-seq peak calling / ChIP-seq Peak calling") and the ALIASES list is largely search-engine padding rather than operational guidance.

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

Completeness3/5

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

With no output schema, the description is the only source for return values, and "peak 清单" is thin. It also leaves the mismatch unexplained: peak calling is normally read/genome/control driven, yet the only input is a generic gene-by-sample matrix, so an agent cannot tell how the matrix is interpreted.

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 data_json parameter is documented in detail (matrix form, rows/cols, optional groups/p_values/log2fc, plus the exact rejection message). The description adds nothing about the parameter, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource: "call peaks from ChIP-seq reads." That is far more informative than the title alone, though it never distinguishes this tool from adjacent omics siblings such as atac_diff or differential_expression.

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

Usage Guidelines4/5

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

The "USE WHEN" clause gives an explicit trigger condition (ChIP-seq reads are the input), which is enough for an agent to know the context. It does not state when NOT to use it, nor point to an alternative for related assays, so it stops short of the top score.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources