LatentContext MCP Server
Server Quality Checklist
Latest release: v1.1.1
- Disambiguation5/5
Each tool targets a distinct operation: compress, forget, retrieve, status, store, and session start. There is no overlap in purpose, and agents can easily distinguish when to use each.
Naming Consistency4/5Five of six tools follow the 'memory_verb' pattern (memory_compress, memory_forget, etc.), but 'session_start' breaks the pattern. This is a minor inconsistency, but the naming is otherwise predictable and readable.
Tool Count5/5Six tools is well-scoped for a memory management server. Each tool serves a clear, essential function without redundancy, covering storage, retrieval, compression, forgetting, status, and session lifecycle.
Completeness4/5Core CRUD-like operations are covered (store, retrieve, forget, compress), along with status and session management. A minor gap is the lack of a cross-session search tool, but this aligns with the design principle of session isolation.
Average 4.6/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description fully discloses session isolation, cross-session contamination absence, and output sections. No contradictory statements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with headings, front-loaded core purpose. Some repetition of session isolation, but overall efficient for the amount of guidance provided.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With nested objects and no output schema, the description adequately covers what to expect (sections), usage scenarios, and tips. Could be more precise on return format, but sufficient for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds usage tips for token_budget (e.g., 5000-8000 for broad context) and explains the query parameter with examples. Schema already covers 67% of parameters, so description adds meaningful but not essential extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves memories from the current session, with emphasis on session isolation. This distinguishes it from siblings like memory_compress, memory_forget, etc., which are for different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when to use: immediately after session_start and before tasks that benefit from context. Lacks explicit when-not-to-use or direct alternatives, but the context is clear and actionable.
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?
No annotations provided, so description fully carries the burden. It discloses output contents (tier counts, token estimates, etc.) and indicates it can show session status. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Concise first sentence, followed by well-organized WHEN TO USE and SHOWS bullet lists. Every sentence serves a purpose; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Completely adequate for a zero-parameter tool: describes purpose, usage guidance, and output. No gaps given the simplicity and no output schema needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0 parameters with 100% coverage, so description adds value by explaining what the tool returns (SHOWS list). Baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb and resource: 'Get storage statistics for all memory subsystems.' The SHOWS list specifies output, distinguishing it from sibling tools like memory_compress or memory_retrieve which have 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 Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit WHEN TO USE bullet list covering debugging, user queries, session check, and budget monitoring. Does not explicitly state when not to use, but the context is clear and sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description fully explains behavioral traits: effects of each action (deprecate, correct, delete), prerequisites (memory_id), and conditional parameter requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with sections, bullet points, and clear headings. Every sentence is informative and concise, no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a modification tool with 3 parameters, conditional logic, and no output schema, the description is comprehensive. It could mention return values or effects on other operations, but current level is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value by explaining the action enum meanings and clarifying the conditional requirement for 'correction' parameter when action='correct'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Mark a memory as outdated, incorrect, or to be deleted.' It distinguishes from siblings (memory_store, memory_retrieve) by focusing on modification/removal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'WHEN TO USE' bullets covering common scenarios. Lacks explicit when NOT to use, but the context is clear. Siblings are listed externally.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It thoroughly explains behavior: proactive storing, content quality mandates, memory types with eviction rules, confidence levels, and entity indexing. This provides complete transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured with clear sections (CRITICAL RULES, WHEN TO USE, MEMORY TYPES). It front-loads the core purpose. Some verbosity from examples is justified for clarity, but could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, 2 required, no output schema, the description is exceptionally complete. It covers content quality, memory types, entities, confidence, and usage contexts. An AI agent has all necessary information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds significant value beyond schema: good/bad examples for content, detailed explanations of each memory_type, and entity requirements. It enhances understanding without contradicting schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool stores information in long-term memory and provides a specific verb and resource. It distinguishes itself from siblings like memory_retrieve by emphasizing proactive use and content quality rules.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes extensive when-to-use guidance (e.g., after learning user info, completing tasks, discovering facts) and content quality rules. However, it lacks explicit when-not-to-use or direct comparisons to sibling tools, though the proactive call to action is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully shoulders the transparency burden. It explicitly states that compression is lossy, original entries are removed, and data is re-embedded. This disclosure is comprehensive for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (WHEN TO USE, EFFECTS). Every sentence adds value, and there is no redundancy. It is appropriately sized for a single-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one param, no output schema), the description is complete. It covers purpose, usage conditions, and behavioral effects without any gaps. An agent can confidently select and invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter exists with 100% schema coverage. The description adds significant meaning beyond the schema by explaining each scope's effect and the requirement for epoch. This enriches the agent's understanding of how to select the appropriate scope.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: compressing memory to reduce token usage and consolidate information. It identifies the specific resource ('memory') and action ('compress'), and distinguishes it from siblings like memory_forget or memory_retrieve.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance for when to use each scope (working, session, epoch), including a prerequisite for epoch (at least 10 session summaries). While it doesn't explicitly state when not to use the tool, the sibling context and clear scope usage cover most decision-making needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description comprehensively covers behavior: creates fresh empty session, ensures no data leakage, returns new session ID, and guarantees isolation. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections and steps, but somewhat verbose. Every part adds value, though could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description adequately explains what is returned (session ID with timestamp-prefixed UUID). It integrates with sibling tools and covers all necessary context for initialization.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, but description adds meaning beyond schema by explaining the session concept and its role. According to guidelines, baseline is 4 for zero params, and this is appropriately descriptive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Start a new memory session' with a specific verb and resource. It distinguishes from sibling tools by emphasizing this is the initialization step for a new conversation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit workflow instructions: call first, then memory_retrieve, then memory_store. It also emphasizes one-time usage per conversation and warns against 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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