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MisakaNet

by org.misakanet

misakanet_me_events

Read-onlyIdempotent

[READ-ONLY EVIDENCE] Return evidence of a lesson being reused (E4 signals): helpful votes, regression-benchmark citations, and cross-node confirmation. Use to check whether a lesson is proven by real usage, not just self-reported. Provide lesson_id or lesson_path — if neither is supplied the tool returns {error}. Semantically 'misakanet_get_my_events' (evidence for the lessons your node submitted/used); kept as me_events for backward compatibility. No auth required (read-only, rate-limited). Returns: object {lesson_id, events: [{type, count|queries|sources, evidence_level}], evidence: 'E0'|'E3'|'E4', note}. Example: misakanet_me_events(lesson_id='dco-auto-fix-workflow')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lesson_idNoLesson ID (filename stem), e.g. dco-auto-fix-workflow. Either lesson_id or lesson_path is required.
lesson_pathNoOptional full path, e.g. lessons/core/dco-auto-fix-workflow.md. Either lesson_id or lesson_path is required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
errorNo
eventsNo
evidenceNo
lesson_idNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed4 schema fields changed
    • addedInput schema / minProperties
      Added value: +1
    • changedInput schema / properties / lesson_id / description
      Previous value: -"Lesson ID (filename stem), e.g. dco-auto-fix-workflow."New value: +"Lesson ID (filename stem), e.g. dco-auto-fix-workflow. Either lesson_id or lesson_path is required."
    • changedInput schema / properties / lesson_path / description
      Previous value: -"Optional full path, e.g. lessons/core/dco-auto-fix-workflow.md."New value: +"Optional full path, e.g. lessons/core/dco-auto-fix-workflow.md. Either lesson_id or lesson_path is required."
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "error": {
+      "type": "string"
+    },
+    "events": {
+      "items": {
+        "properties": {
+          "count": {
+            "type": "number"
+          },
+          "evidence_level": {
+            "type": "string"
+          },
+          "queries": {
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          "sources": {
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          "type": {
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "evidence": {
+      "enum": [
+        "E0",
+        "E3",
+        "E4"
+      ],
+      "type": "string"
+    },
+    "lesson_id": {
+      "type": "string"
+    },
+    "note": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints, but the description adds non-obvious behavior: no auth required, rate-limiting, the {error} response for missing identifiers, and the backward-compatibility reason for the me_events name. Nothing contradicts the annotations.

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?

The description is dense but front-loaded with the core purpose, then covers invocation, auth, return shape, and an example. There is slight redundancy with the annotations and output schema, but every sentence contributes useful context.

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 two-parameter read-only tool with an output schema, the description covers invocation requirements, failure behavior, authentication, rate limiting, return structure, and an example. An agent has everything needed to select and invoke it correctly.

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 coverage is 100%, and each parameter description already documents the either-or requirement. The description reinforces that requirement and provides an example value, but it does not add substantial semantic information beyond what the input schema already supplies.

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 opens with a specific verb and resource: 'Return evidence of a lesson being reused (E4 signals)'. It names concrete signals like helpful votes, regression-benchmark citations, and cross-node confirmation, and clarifies that the tool checks real usage rather than self-reported claims.

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?

It explicitly says when to use the tool: 'Use to check whether a lesson is proven by real usage, not just self-reported.' It also states the invocation requirement to provide lesson_id or lesson_path, and notes the error if neither is supplied. However, it does not name alternatives or give explicit when-not-to-use conditions.

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

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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a distinct role: retrieval (search/get_lesson), submission (submit_intake/write_lesson), evidence (me_events), risk preflight, and registration. The two submission tools are carefully differentiated by structure and auth requirements, so an agent should not confuse them.

Naming Consistency4/5

All tools share the misakanet_ prefix and snake_case style, and most use a verb_noun pattern. Minor deviations like search, register, preflight, and me_events break the pattern slightly, but the names remain readable and predictable.

Tool Count5/5

Seven tools is well-scoped for a failure-lesson registry: one onboarding tool, one guardrail, two read paths, two submission paths, and one evidence-check tool. No tool feels redundant or missing at a coarse level.

Completeness5/5

The lifecycle is covered end-to-end: register for access, search and get lessons, submit either partial intakes or structured lessons, preflight risky actions, and check reuse evidence. Update/delete are intentionally absent because lessons are immutable, and corrections route through intake, so there are no dead ends.

Resources