mindcore-memory-mcp

Server Quality Checklist

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Latest release: v0.1.15
Disambiguation5/5
Each tool has a clearly distinct purpose: BND algorithm evaluation, deduction, memory CRUD operations, context building, and stats. No overlapping functionality.
Naming Consistency4/5
Tools follow a consistent snake_case convention with prefixes like 'bnd_' and 'memory_', though 'deduce' deviates from the verb_noun pattern and 'memory_context' is noun_noun instead of verb_noun.
Tool Count5/5
9 tools is well within the ideal 3-15 range, covering all essential operations for a memory system with cognitive algorithms without being excessive.
Completeness4/5
Covers core memory operations (store, recall, delete, update confidence, stats, context) and specialized BND/deduction tools. Minor gaps like inability to update other memory fields or list all memories are acceptable.
Average 3.4/5 across 9 of 9 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 82 commits in the last 12 weeks
- Last stable release on June 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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?
With no annotations provided, the description should disclose behavioral traits like read-only nature, authentication requirements, or relevance scoring. It only says 'recall memories,' offering no transparency on side effects or underlying behavior.
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 short sentence, which is efficient with no extraneous words. However, it sacrifices informativeness for brevity.
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 4 parameters, no output schema, and no annotations, the description is too sparse. It fails to explain query behavior, result format, filtering logic, or error handling, leaving the agent with significant ambiguity.
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?
Input schema has 100% description coverage, so the baseline is 3. The description adds no additional meaning beyond the schema; it does not explain how parameters like tags or session_id affect the query.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Recall memories relevant to the query.' uses a specific verb and resource, but it is vague and does not distinguish from sibling tools like memory_context or memory_stats. It lacks detail on what 'recall' means (search vs. retrieve) and how it differs from similar operations.
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 such as memory_context or memory_stats. There is no mention of prerequisites, use cases, or when not to use this tool.
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 present, and the description only states 'store' and 'long-term' without clarifying behavioral traits like idempotency, authorization requirements, side effects, or return values. The description is too minimal to provide adequate transparency.
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 sentence of only 8 words, effectively front-loading the core purpose. Every word is necessary and there is no redundancy.
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 6 parameters (including optional ones like tags, source, confidence, importance, session_id) and no output schema, the description is too brief. It omits details about storage behavior, error handling, and return values, making it incomplete for effective 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?
All 6 parameters have descriptions in the input schema (100% coverage). The description adds no additional meaning beyond the schema, so a baseline score of 3 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 the action ('store'), the resource ('memory'), and the purpose ('long-term'). It distinguishes the tool from siblings like memory_recall and memory_delete by focusing on storing new memories.
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 such as memory_context or memory_update_confidence. There is no mention of prerequisites, when not to use it, or contextual hints.
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 disclose behavioral traits. It only states it returns stats, but does not clarify if the operation is read-only, potentially expensive, or requires any permissions. For a safe retrieval tool, the description fails to confirm its non-destructive nature or any 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous information. Every word contributes to conveying the tool's function efficiently.
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 simple no-parameter tool, the description provides the key output elements (count, distribution, confidence). Although there is no output schema, the listed stats give sufficient context. However, the absence of format details or response structure slightly reduces completeness.
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?
There are no parameters, and schema coverage is 100% (no params to describe). The description does not need to add parameter semantics. Baseline for 0 parameters is 4, and the description meets that baseline.
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 retrieves memory statistics (total count, importance distribution, avg confidence). It uses a specific verb ('Get') and lists the returned data, making the purpose unambiguous. However, it does not distinguish itself from sibling tools like bnd_stats, which might also provide statistical information.
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 such as memory_recall or memory_context. The description lacks context for preferred usage scenarios or when to avoid it.
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 provided; description lacks disclosure of side effects (e.g., whether it modifies memories), resource usage, or what 'high-quality COG memories' means.
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?
Two concise sentences with no fluff, front-loading the action and outcome.
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?
Missing output schema and description of return format; 2 parameters and no annotations leave gaps for a tool that derives insights.
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 100%, so baseline is 3; description repeats schema definitions without adding extra meaning or format details.
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?
Description clearly states 'run the Deduction engine on stored memories to derive new cognitive insights' and 'finds patterns across high-quality COG memories,' distinguishing it from sibling memory and search tools.
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?
Implies use when pattern derivation is needed, but no explicit when-to-use or when-not-to-use guidance, nor alternatives mentioned.
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 carries the full burden. It discloses the algorithm name and output format but does not reveal behavioral traits like side effects, authentication needs, rate limits, or whether the call is safe. This is insufficient for a potentially complex evaluation tool.
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 two sentences with no filler or repetition. It front-loads the action and outputs, making it efficient for an AI agent to parse.
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 and no annotations, the description explains the tool's core function and output but lacks details on parameter usage, prerequisites, error handling, or return value structure. It is minimally adequate but has gaps.
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% with basic parameter descriptions. The description adds high-level output context (4D scores and decision) but does not explain how parameters like tags, confidence, or importance affect the evaluation or algorithm. Baseline 3 is appropriate because schema already documents parameters.
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 specifies a verb ('evaluate'), a resource ('content'), and a named algorithm ('3D Boundary balance algorithm'). It lists the exact output dimensions (TRJ/EVO/COG/BALANCE) and an accept/reject decision, distinguishing it clearly from sibling tools like bnd_stats or deduce.
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 for evaluating content via a specific algorithm, but it does not explicitly state when to use it versus alternatives (e.g., bnd_stats) or when not to use it. No exclusions or context are provided.
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, the description carries full burden but only states 'Build a context window' without disclosing behaviors: whether it modifies state, what 'relevant' means (e.g., similarity-based?), auth requirements, or limits. Critical behavioral traits are missing.
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 sentence of 9 words, containing no filler. It is front-loaded with the action and purpose. Every word earns its place.
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 should clarify return format or structure but only says 'context window'. It does not explain what 'relevant' means, error handling, or limits. Adequate for a simple tool but missing important details.
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%, so all three parameters have clear descriptions in the schema. The tool description adds no additional meaning beyond what the schema already provides. Baseline score of 3 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 the action (Build), the resource (context window from relevant memories), and the purpose (for LLM input). It distinguishes from siblings like memory_recall by specifying the output is a context window, not just recalling memories.
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 when to use (need a context window for LLM), but provides no explicit guidance on when not to use it or how it differs from siblings like memory_recall or memory_store. Usage context is implied rather than directed.
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 provided, so description carries full burden. Only states 'update' without disclosing irreversibility, permissions, or side effects on memory storage. Lacks depth for a mutation operation.
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?
Single sentence of 6 words, extremely concise with no redundant information. Every word serves a purpose.
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?
Minimally adequate for a simple update tool with full param coverage and no output schema. But lacks usage guidance and behavioral details to fully support agent decision-making.
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% with descriptions for both parameters. Description adds 'after correction' context but does not enhance parameter meaning beyond what schema provides.
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?
Description clearly states specific action 'update' on resource 'memory confidence score' with context 'after correction'. Distinguishes from siblings like memory_store, memory_delete, memory_recall, and memory_stats.
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?
Implied usage 'after correction' but no explicit when-to-use or when-not-to-use guidance. No alternatives or exclusions mentioned despite having related siblings.
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 provided, so description bears full burden. It does not disclose behavioral traits such as whether it is read-only, if it has side effects, or any rate limits. Only states it 'gets' stats, which implies no changes but not explicitly.
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?
A single, front-loaded sentence that succinctly conveys the tool's purpose with no extraneous words. Every part is necessary and informative.
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 no output schema, the description lists the types of stats returned, which is sufficient for an agent to understand the result. However, it could mention return format or time period for completeness.
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?
No parameters exist, so schema coverage is 100%. Baseline for 0 parameters is 4. Description adds no parameter info, but none is needed.
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 it gets BND manager stats and lists specific types: acceptance rate, score distributions, anti-chain triggers. The verb 'Get' and resource 'BND manager stats' are clear, and it distinguishes from sibling tools like bnd_check and memory tools.
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 on when to use this tool versus alternatives like bnd_check. No context provided for appropriate usage scenarios 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?
Describes irreversibility, a key behavioral trait beyond the schema. No annotations exist to cover safety, so the description partially compensates but could detail more (e.g., permission needs, cascading effects).
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?
Two efficient sentences; no filler. Purpose and caution front-loaded.
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 simple one-param delete tool with no output schema or annotations, it conveys essential info (purpose, irreversibility). Minor gap: no note on error conditions or access.
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%, so baseline is 3. Description adds no extra meaning beyond the schema's 'The memory ID to delete.'
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?
Clearly states 'Delete a memory by its ID' — specific verb+resource. Distinguishes from siblings like memory_store, memory_recall.
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?
Explicitly warns 'Irreversible — use with caution,' providing clear usage context. Lacks explicit when-not-to-use or alternatives.
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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