About agentsnap
agentsnap-mcp is an MCP server for inspecting and testing AI agent tool-call traces. Developers can normalize traces, compare runs, and validate saved snapshots to detect changes and regressions.
Key features
- 01Normalize traces and generate fingerprints
- 02Diff baseline and current traces
- 03Classify trace changes and regressions
- 04Validate snapshots against the trace schema
- 05Ignore selected paths during comparisons
Use cases
- 01Check agent traces between releases
- 02Identify changed tool calls or outputs
- 03Validate saved trace snapshots
- 04Filter noisy fields from regression checks
MCP support
The MCP server exposes tools to normalize, diff, and validate agent tool-call traces for MCP clients such as Claude Desktop, Cursor, Cline, Windsurf, and Zed.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
3Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| input | string | No | Optional input string to embed in trace.input. |
| model | string | No | Optional model identifier to embed in trace.model. |
| trace | any | Yes | Either an array of tool-call entries (each {name, args, result?, error?}) or a partial Trace object with a tools[] field. |
| output | string | No | Optional output string to embed in trace.output. |
Details
From the project READMEagentsnap-mcp
An MCP server that gives AI assistants the ability to inspect, normalize, diff, and validate agent tool-call traces.
Built on top of @mukundakatta/agentsnap.
Works with Claude Desktop, Cursor, Cline, Windsurf, Zed, and any other MCP client.
Tools exposed
normalize_trace
Coerce a raw tool-call trace into the canonical agentsnap Trace shape and compute a deterministic SHA-256 fingerprint hash. Use this to compare runs cheaply or feed downstream diff tools.
{
"trace": [
{ "name": "search", "args": { "q": "cats" } },
{ "name": "fetch", "args": "https://example.com" }
],
"input": "find cats",
"model": "claude-opus-4-7"
}
→
{
"normalized": {
"version": 1,
"model": "claude-opus-4-7",
"input": "find cats",
"output": null,
"tools": [
{ "name": "search", "args": { "q": "cats" } },
{ "name": "fetch", "args": "https://example.com" }
],
"error": null,
"fingerprint": { "node": "v22.0.0", "agentsnap": "0.1.0" }
},
"hash": "sha256:..."
}
diff_traces
Diff a baseline trace against a current run. Returns a uniform
additions / removals / changes vocabulary plus the agentsnap status code
(PASSED, OUTPUT_DRIFT, TOOLS_REORDERED, TOOLS_CHANGED, REGRESSION).
Use ignore_paths to silence noisy fields before classification.
{
"baseline": { "version": 1, "tools": [{ "name": "search", "args": { "q": "cats" }, "result_hash": "sha256:aaa" }], "error": null, "fingerprint": {...} },
"current": { "version": 1, "tools": [{ "name": "search", "args": { "q": "cats" }, "result_hash": "sha256:bbb" }], "error": null, "fingerprint": {...} },
"ignore_paths": ["tools[].result_hash"]
}
→ { "same": true, "status": "PASSED", "additions": [], "removals": [], "changes": [] }
validate_snapshot
Sanity-check a snapshot against the agentsnap Trace schema. Verifies required
fields, tool-entry shape, and surfaces actionable issues. Returns
valid=true on success or a list of human-readable problems.
Install
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"agentsnap": {
"command": "npx",
"args": ["-y", "@mukundakatta/agentsnap-mcp"]
}
}
}
Cursor / Cline / Windsurf / Zed
Same shape, in the appropriate mcp.json for your client. Most clients
auto-discover via npx -y @mukundakatta/agentsnap-mcp.
Local install
npm install -g @mukundakatta/agentsnap-mcp
mcp-agentsnap # listens on stdio
Why this matters
Agents that call tools drift silently. A tool argument changes shape, a nondeterministic result flips a downstream branch, an extra tool sneaks in between releases — none of it shows up in unit tests. agentsnap captures the trace; this MCP server lets your assistant inspect and reason about traces directly: normalize one, diff two, or validate a saved snapshot, all from the model's tool-use surface.
License
MIT.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.