Metacognitive Compute Scheduler

by LZF1111

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct and specific purpose: session management, decision-making, outcome reporting, calibration, etc. There is no overlap or ambiguity between tools.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern (e.g., close_session, decide_step), but 'new_task' uses an adjective_noun pattern and 'task_feedback' is ambiguous, causing minor inconsistency.
Tool Count5/5
With 9 tools covering session lifecycle, decision, learning, and monitoring, the count is well-scoped for the intended domain without being excessive or insufficient.
Completeness5/5
The tool set covers the full metacognitive scheduling cycle: session/task initiation, step decision, outcome reporting, feedback, calibration, and prototype management. No obvious gaps.
Average 3.1/5 across 9 of 9 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 25 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions 'persist skills' but does not elaborate on side effects, whether the action is destructive, or state changes. With no annotations, more detail is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short but at the expense of completeness. It lacks necessary detail, making it under-specified for effective tool selection.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple schema (1 param, no output), the description still fails to provide sufficient context about session lifecycle, return values, or effects of persisting skills.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage. The description does not explain what 'sessionId' represents or its format, leaving the agent to infer meaning only from the type string.
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 '关闭会话并持久化技能' clearly indicates the tool closes a session and persists skills. It is a verb-resource pair that distinguishes it from siblings like 'open_session'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, conditions, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It reveals that the tool is a read operation returning a set of metrics, which implies safety but does not explicitly state whether it is idempotent or has side effects. It gives some behavioral context but lacks details on rate limits, permissions, or return format.
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 a single sentence, making it concise. It front-loads the purpose and lists key return values. However, it is slightly under-specified because it omits parameter details; it is efficient but not fully informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and low parameter coverage, the description is the sole source of information. It lists some return metrics but omits data types, error conditions, and usage context. For a stat retrieval tool, it is incomplete and could be more helpful with additional details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'sessionId' has no description in the input schema (0% coverage). The tool description does not mention the parameter or explain its format, constraints, or purpose. The description adds no value beyond what the schema already provides, which is nothing.
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 clearly states the purpose: checking the current state of a session, listing specific metrics like prototype count, μ, ignition count, steps, and context pollution. It is specific about what the tool does, though it does not explicitly differentiate from siblings like dump_prototypes or get_calibration, which likely have distinct purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, conditions, or exclusions. The meaning of 'session state' is implied but not detailed enough to help an agent choose between this and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: adjusting a coordination variable μ and automatically persisting the prototype library. However, with no annotations, it fails to mention potential side effects like disk writes, required permissions, or whether success/failure is final.
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 description is a single sentence, which is concise, but the structure is dense and could be split for clarity. It front-loades the main purpose but includes multiple actions in one clause.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool performs three actions (report, adjust, persist) with no output schema and no parameter descriptions. The description omits important context: what 'μ' is, what 'prototype library' refers to, and how success/failure are reported.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no information about the parameters sessionId or success. An agent would need external documentation to understand their formats or valid values.
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 specifies the action (report success/failure), the resource (task outcome), and additional effects (adjust μ, persist prototypes). It distinguishes from sibling tools like report_outcome by including these extra steps, though the distinction could be clearer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs. alternatives like report_outcome or dump_prototypes. The phrase 'after the entire task ends' implies context but lacks exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden. It explains that the tool returns metrics over a rolling window and the meaning of MAE and accuracy, but does not disclose if it is read-only or has side effects.
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 concise, with two sentences that front-load the main purpose. It includes helpful parenthetical clarifications, but slightly verbose with the Chinese phrasing.
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?
Given no output schema, the description explains the return components (MAE, accuracy, halves, improving boolean) but lacks detail on structure, types, or rolling window specifics. Adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% parameter coverage with sessionId as the only parameter, but the description provides no explanation of this parameter. Schema coverage is 0%, and the description fails to add meaning beyond the schema.
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 clearly states the tool returns calibration metrics (MAE and accuracy) within a rolling window, split into halves with an improving boolean. It specifies a distinct purpose, but does not explicitly differentiate from sibling tools like 'get_stats'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the tool is 'used to prove that as tasks increase, accuracy improves', giving some context. However, it lacks explicit guidance on when to use vs. alternatives, and no exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only describes the output content, but does not mention whether the operation is read-only, destructive, requires authentication, or has rate limits. The lack of behavioral context leaves ambiguity about safety and side effects.
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 a single, concise sentence in Chinese that front-loads the action and includes a clarifying parenthetical. It is efficient with no wasted words, though it could be structured to include parameter info without adding much length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and no output schema, the description should provide more details on the parameter and the return format. It mentions the output contains 'centroid and weight' but lacks specifics on structure, datatypes, or how to interpret the data. The absence of parameter description makes the tool less usable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the lone required parameter 'sessionId'. The description focuses solely on the output, offering no guidance on the parameter's purpose or expected values. This is a critical gap for a tool with a single required parameter.
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 explicitly states the tool exports a prototype library with specific content (situation centroid and read weight), and indicates its utility for audit/migration. It uses a clear verb '导出' (export) and distinguishes itself from the listed siblings like get_stats or get_calibration, which serve different purposes.
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 description mentions the tool can be used for 'audit/migration', providing clear context for when to use it. However, it does not explicitly state when not to use it or compare it to alternative tools, but the implied usage is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It states it returns a sessionId and implies session creation, but it does not mention side effects, idempotency, or what happens if the same sessionId is used twice. This is insufficient for a tool that likely persists state.
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?
Two concise sentences: first states purpose, second explains namespace and return value. No unnecessary text. The structure is clear and front-loaded, though it could benefit from a bullet point or clearer separation of concepts.
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?
The description mentions the return value (sessionId) and its usage for subsequent calls. However, without an output schema, it should explain more about the session's lifecycle, such as the need to call close_session or whether sessions can be reopened. It is adequate but not 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?
The input schema has 100% coverage with descriptions for both parameters. The description adds context about namespace deciding skill library reuse, which somewhat extends the schema's description. However, it does not add syntactic details, so baseline 3 is appropriate.
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 clearly states 'open a scheduling session' and explains the role of namespace in reusing skill libraries. It distinguishes from siblings like close_session and decide_step, though it could be more precise about what a 'scheduling session' entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use the tool: when you need to open a session with a specific namespace for skill reuse. However, it lacks explicit guidance on when not to use it or alternatives (e.g., if a session already exists, should you close it first?). The namespace explanation provides context but no exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behavioral traits: resetting loop-level self-state but retaining cross-task prototypes and caution. This is fairly transparent, though it could detail more about the nature of these items. Score 4 due to useful disclosure beyond a minimal statement.
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 a single, front-loaded sentence in Chinese that efficiently conveys the core action and behavioral changes. Every word earns its place with no redundancy. Exceptionally concise and well-structured.
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?
The description covers the main action and key behavioral consequences but omits important context: no explanation of the sessionId parameter, no return value description (no output schema), and no mention of prerequisites like an open session. Moderate completeness given the simple tool shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not mention the sole parameter 'sessionId' at all. With a single required parameter and no description, the agent receives no guidance on what to provide. Baseline is not applicable as there is a parameter; 1 reflects complete lack of semantic contribution.
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 clearly states the tool starts a new task with specific side effects (resetting loop state, preserving prototypes and caution). While it distinguishes implicitly from sibling tools like open_session and close_session, it does not explicitly differentiate, so a 4 is appropriate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used when starting a new task, but provides no explicit guidance on when to use it versus alternatives (e.g., open_session, decide_step) or any prerequisites. Score 3 reflects implied usage without exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the learning side-effect ('自学', '生长/细化原型') and parameter meanings, but is vague about what 'reporting' entails (e.g., state changes, return values).
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 concise (a few sentences) and front-loaded with the main action. It efficiently explains parameter meanings, though the structure is somewhat informal.
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?
Given 7 parameters (3 required) and no output schema, the description covers parameter purpose but lacks details on return values, timing relative to other steps, and precise lifecycle context. It assumes familiarity with the system.
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?
Schema description coverage is only 14%, but the description explains observed_criticality, used_system2, and was_deep in detail, and clarifies the role of criticality_hint/difficulty_hint/progress. This compensates for the lack of schema descriptions on parameters.
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 clearly states the tool's action: report real outcomes after a step for self-learning. It explains specific fields (observed_criticality, used_system2, was_deep), but does not explicitly differentiate from sibling tools like decide_step or task_feedback.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage occurs after a step ('这一步做完后') and recommends carrying over hints from decide_step. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It explains inputs and output but does not disclose internal logic, potential side effects, or deterministic behavior. It hints at being a decision tool but lacks depth on how the decision is made or any constraints.
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 concise (about 100 Chinese characters) and packs purpose, input list, and output into a single sentence. It is front-loaded with the core goal. Minor improvement would be structuring with bullet points, but it is efficient for the content.
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?
Given moderate complexity (5 parameters, no output schema), the description covers the tool's purpose, input semantics, and output nature. It lacks explicit return value structure (e.g., keys in response) but states '返回 mode 及理由', which is sufficient for basic use.
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 80%, meaning most parameters are already documented. The description adds context that inputs are 0-1 observables and briefly explains each parameter (e.g., '表面多关键'). This adds some value beyond the schema but does not significantly enhance parameter meaning.
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 the tool's purpose: to decide between System1 (intuition/cheap model) and System2 (strong model/deep reasoning) for the current step. It uses specific verbs ('判断', '该用') and identifies the resource ('当前这一步'). It is distinct from sibling tools like report_outcome or close_session, which handle other aspects.
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 description explicitly lists the required inputs (criticality_hint, difficulty_hint, progress, context_pollution) and explains they are observable measures. It states the output (mode and reason) and clarifies this is a meta-cognitive decision orthogonal to picking steps. However, it does not provide explicit when-to-use or when-not-to-use guidance beyond the implied context.
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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metacognitive-compute-scheduler-EMMS-messo139 MCP server

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