Peptide Library

Get human evidence summary

get_evidence_summary

Read-onlyIdempotent

What reviewed human research shows for one peptide, claim by claim: each claim under investigation, its GRADE certainty per outcome (or that it is not yet assessed), the distinct human studies assessed and the evidence gaps, with links to every assessment. Only published assessments, each marked automated (AI, not human-reviewed) or reviewed. Never dosing or administration guidance.

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": {
      "peptide": "<peptide>"
    },
    "name": "get_evidence_summary"
  }
}

Input Schema

Name	Required	Description	Default
`peptide`	Yes	A peptide id (e.g. "bpc-157"), name, synonym or alias (e.g. "PT-141", "Ozempic").

Output Schema

Name	Required	Description
`gaps`	Yes
`claims`	Yes
`counts`	Yes
`source`	Yes
`status`	Yes	assessed: at least one reviewed, published record exists.
`peptide`	Yes
`studies`	Yes
`disclaimer`	Yes
`how_to_read`	Yes
`evidence_url`	Yes
`retrieved_at`	Yes
`methodology_url`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 6, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantive behavior: results are limited to published assessments, each tagged as automated AI output versus human-reviewed, and gaps are surfaced rather than omitted. The AI-vs-reviewed labeling is a meaningful trust signal an agent could not infer from annotations alone.

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?

Front-loaded with the core value proposition, then a colon-delimited enumeration of returned fields, then caveats. It is a single dense sentence that borders on over-packed, but every clause carries information and nothing is redundant.

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?

For a read-only lookup with one well-documented parameter and an output schema, the description covers purpose, scope, trust labeling, and an explicit exclusion. Only the lack of explicit sibling routing keeps it from being fully complete.

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?

With a single parameter at 100% schema description coverage, the schema already explains accepted forms (id, name, synonym, alias). The description only reinforces the 'one peptide' scope; it adds no format or edge-case detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('what reviewed human research shows for one peptide') and enumerates the exact payload: per-claim GRADE certainty, distinct human studies, evidence gaps, and links. This is clearly distinguishable from siblings like get_human_study (a single study) or search_research, without opening either schema.

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?

Gives clear scoping context: one peptide at a time, only published assessments, and an explicit exclusion ('Never dosing or administration guidance'). It does not name a sibling alternative (e.g. get_human_study for a single study) or state prerequisites, so it stops short of full when/when-not routing.

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

All Peptide Library tools Inspect

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