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PlanExe

by io.github.PlanExeOrg

example_prompts

Read-onlyIdempotent

Call this first. Returns example prompts that define what a good prompt looks like. Do NOT call plan_create yet. Optional before plan_create: call model_profiles to choose model_profile. Next is a non-tool step: formulate a detailed prompt (typically ~300-800 words; use examples as a baseline, similar structure) and get user approval. Good prompt shape: objective, scope, constraints, timeline, stakeholders, budget/resources, and success criteria. Write the prompt as flowing prose, not structured markdown with headers or bullet lists. Weave technical specs, constraints, and targets naturally into sentences. Include banned words/approaches and governance preferences inline. The examples demonstrate this prose style — match their tone and density. Then call plan_create. PlanExe is not for tiny one-shot outputs like a 5-point checklist; and it does not support selecting only some internal pipeline steps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYes
samplesYesExample prompts that define the baseline for what a good prompt looks like. Take inspiration from these when writing your own prompt for plan_create (typically ~300-800 words). Good prompt shape: objective, scope, constraints, timeline, stakeholders, budget/resources, and success criteria.

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "message": {
+      "title": "Message",
+      "type": "string"
+    },
+    "samples": {
+      "description": "Example prompts that define the baseline for what a good prompt looks like. Take inspiration from these when writing your own prompt for plan_create (typically ~300-800 words). Good prompt shape: objective, scope, constraints, timeline, stakeholders, budget/resources, and success criteria.",
+      "items": {
+        "type": "string"
+      },
+      "title": "Samples",
+      "type": "array"
+    }
+  },
+  "required": [
+    "samples",
+    "message"
+  ],
+  "title": "ExamplePromptsOutput",
+  "type": "object"
+}
  2. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral guidance beyond annotations: it mandates the tool to be called first, warns against calling plan_create prematurely, and outlines the exact downstream workflow and prompt-style expectations. This is valuable context that annotations do not convey.

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?

The description is long but every sentence earns its place. It front-loads 'Call this first' and then provides a logically ordered sequence of actions, prompt shape guidelines, stylistic instructions, and a clear next step. Nothing is redundant or filler; the length is justified by the need to explain the non-tool step and workflow.

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?

Given there are no parameters and the tool has an output schema, the description is fully complete. It explains the tool's purpose, its place in the overall workflow, alternatives, and detailed follow-up actions. The agent has everything it needs to correctly invoke and use this tool.

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

Parameters4/5

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

The tool has zero parameters, so the baseline is 4. The description doesn't need to explain parameter semantics, and it doesn't; it focuses on usage guidance. Since there are no parameters to misinterpret, this score is appropriate.

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 clearly states that the tool returns example prompts and defines what a good prompt looks like. It also distinguishes this tool from siblings by positioning it as the first step and explicitly telling the agent not to call plan_create yet, making its unique role obvious.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance: call this first, do not call plan_create yet, optionally call model_profiles, then follow a non-tool step, and only then call plan_create. It also includes clear exclusions for the overall workflow (PlanExe is not for tiny outputs and doesn't support selecting only some steps), giving an agent all the context needed to decide when this sequence is appropriate.

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, non-overlapping purpose within the PlanExe workflow. For example, example_plans and example_prompts serve different preparatory functions, while plan_create, plan_status, plan_stop, plan_resume, and plan_retry handle distinct lifecycle stages of plan generation. No tools appear to duplicate functionality, making selection clear for an agent.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun pattern throughout, with all tools using snake_case and clear action-object pairs (e.g., plan_create, plan_status, plan_stop). The naming is predictable and readable, with no deviations in style or convention across the set.

Tool Count5/5

With 11 tools, the server is well-scoped for its purpose of generating and managing strategic project plans. Each tool serves a specific role in the workflow, from preparation (example_prompts, model_profiles) to creation (plan_create), monitoring (plan_status), lifecycle management (plan_stop, plan_resume, plan_retry), and feedback (send_feedback). The count is appropriate and avoids bloat or gaps.

Completeness5/5

The tool set provides complete coverage for the plan generation domain, including CRUD-like operations (create, list, status, stop, resume, retry) and supporting functions (examples, model selection, file retrieval, feedback). There are no obvious gaps; agents can handle the entire lifecycle from prompt drafting to plan retrieval and error recovery without dead ends.

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