Linksee Memory

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.2.2
Disambiguation5/5
Each tool has a clear, distinct purpose: memory storage (remember), editing (update_memory), retrieval (recall, recall_file, list_entities), deletion/consolidation (forget, consolidate), and file caching (read_smart). There is no overlap that would cause misselection.
Naming Consistency4/5
Most tools follow a verb or verb_noun pattern (e.g., consolidate, forget, list_entities, recall, recall_file, remember, update_memory). 'read_smart' breaks this pattern with a verb_adjective form. Overall, the naming is mostly consistent with minor deviations.
Tool Count5/5
With 8 tools, the count is well within the ideal range for a memory management server. Each tool serves a necessary function without being redundant or excessive.
Completeness4/5
The tool set covers the core CRUD operations for memories, along with consolidation and file-specific retrieval. A direct 'get_memory_by_id' tool is missing, but recall can retrieve specific memories, so the gap is minor and workable.
Average 4.6/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 74 commits in the last 12 weeks
- Last stable release on April 20, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
View related servers

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden of disclosing behavior. It explains that the tool is destructive ('deletes originals') and runs a 'forget-sweep'. It mentions that results are 'protected learning-layer entries' and that dry_run previews without writing. While some terms like 'forget-sweep' are not further explained, the overall destructive nature and side effects are transparent. Score 4 for solid disclosure.
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, front-loading the primary action and listing key steps. Every sentence adds value: first defines the operation, second gives usage guidance. No fluff or redundancy. This is a model of concise yet informative description.
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 there is no output schema, the description should explain what happens and what the output looks like. It covers the process (clustering, summarizing, deleting, forget-sweep) and mentions dry_run. However, it omits details on the 'forget-sweep' and what 'protected learning-layer entry' means. For a tool with 3 parameters and no output schema, this is reasonably complete, but leaves minor gaps.
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 67% (2 of 3 params have descriptions). The tool description adds context around 'cold low-importance memories' which helps interpret the min_age_days parameter. It also reinforces dry_run usage. The scope parameter's enum values are not elaborated in the description, but the tool's domain implies session vs all. Overall, the description adds meaningful context beyond the schema, scoring a 4.
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 action: 'Sleep-mode compression. Clusters cold low-importance memories by (entity, layer), summarizes each cluster into a single protected learning-layer entry, deletes originals, and runs a forget-sweep.' It provides a specific verb (compress/cluster/summarize/delete) and resource (memories). This clearly distinguishes it from sibling tools like 'forget' (individual deletion) or 'remember' (saving).
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 recommends when to use: 'Run at session end or on demand.' It also advises on dry_run usage. However, it does not explicitly state when not to use it or provide alternatives, such as using 'forget' for single-item deletion. The context is clear but lacks exclusionary guidance, scoring a 4.
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 carries full burden. It discloses sorting behavior and states it is cheaper than recall, but does not mention any specific permissions or side effects. For a list tool, this is adequate but not fully transparent.
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?
Three sentences, no redundancy. First sentence states purpose, second gives usage context, third adds cost comparison. Every sentence adds value.
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?
No output schema, but description explains return is list of entities sorted by recent activity. Mentions cost comparison and usage context. Could detail return format more but sufficient for low complexity.
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 descriptions cover 75% of parameters with defaults and enum details. Description adds no additional meaning beyond the schema, so baseline 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?
Description clearly states 'List the entities currently known to this memory store, sorted by recent activity' with specific verb and resource. It distinguishes from sibling tools like recall by noting it is for overview before specific queries.
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?
Explicitly advises use at session start for 'what do I know about?' before specific recall queries. Compares cost to recall tool, providing clear context for when 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses what data is returned (edit count, daily breakdown, intents, linked memories) and that it queries session_file_edits. However, it does not address potential issues like multiple file matches or performance implications.
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 sentences plus return list: efficient, front-loaded with key information, no wasted words.
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 2 params, no output schema, and no annotations, the description explains return values (edit count, daily breakdown, intents, linked memories) but lacks details on format, error handling, or behavior for multiple matches.
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 both parameters already described. The description does not add significant new meaning beyond what the schema provides, so baseline 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?
Description clearly states verb 'Get' and resource 'edit history of a file', emphasizes 'COMPLETE' and 'per-edit user-intent context', and distinguishes from sibling recall() by specifying it queries session_file_edits.
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?
Explicitly tells when to use: 'when you need to understand WHY a file was modified historically' and contrasts with recall(), providing clear guidance on tool selection.
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 discloses the caching behavior and all four possible return states based on file modification status, offering 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise and well-structured with numbered return cases. While informative, it could be slightly more compact without losing clarity.
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?
Despite lacking an output schema, the description thoroughly explains all four possible return types and the effect of the 'force' parameter, making it complete for an agent to understand the tool's behavior.
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% coverage with descriptions for both parameters. The tool description does not add additional semantic value beyond what the schema already provides, 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 tool reads a file with diff-only caching and lists four distinct return scenarios. It explicitly distinguishes itself from an alternative 'Read' tool by advising when to use it instead.
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 explicitly states 'Use INSTEAD of Read for files you have read before — saves 50%+ tokens on re-reads', providing clear guidance on when to use this tool vs. an alternative.
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, but the description fully covers behavioral aspects: search method (BM25+entity), re-ranking composite score, token budget, match_reasons, pagination, and side effect of refreshing stale momentum scores. This is highly transparent.
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?
Four concise sentences, each adding significant information. Starts with core action, then details, then usage guidance. No wasted words.
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?
Covers retrieval mechanism, ranking, token budget, match_reasons, side effect, pagination, layer aliases, and usage context. Lacks explicit description of return format (fields of memory objects), but given no output schema, it mentions match_reasons, which is key. Still fairly complete.
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 100%, so baseline 3. The description adds value by explaining the composite score, pagination using has_more, layer alias acceptance, and the token budget mechanism, which enrich understanding beyond individual parameter descriptions.
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 it retrieves memories using full-text search and entity-name match, with specific re-ranking. It distinguishes from siblings by focusing on relevance to current context and mentions 'Use at the start of any task that might involve prior work', providing clear purpose.
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 says when to use ('at the start of any task that might involve prior work'). However, it does not mention when not to use or provide explicit alternatives among siblings. Still, the context is clear.
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?
Discloses that the tool is destructive, specifies preserved memory types (caveat-layer, goal-layer, pinned), and mentions the dry_run capability via schema. However, the description does not detail the exact forgettingRisk formula or that deletion is irreversible, but the dry_run parameter is well-described in schema.
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 three sentences long, with the main action stated first. It is concise and free of unnecessary detail, efficiently communicating key information.
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 the tool's complexity (two modes, preservation logic, destructive nature), the description covers essential aspects. It explains when to use each mode, what is preserved, and the destructive intent. No output schema is provided, but the description does not need to discuss return values as the tool's effect is primary.
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 description adds semantic context for memory_id (explicit deletion) but does not explicitly mention dry_run. However, it enriches the understanding by explaining the two operational modes and preservation rules, which go beyond the schema's parameter descriptions. Schema coverage is 50%, but the description compensates with broader context.
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 two distinct modes: explicit deletion by ID and auto-forgetting based on forgettingRisk. It also specifies preservation of caveat-layer, goal-layer, and pinned memories. This distinguishes it from siblings like update_memory and remember.
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?
Explicitly advises to prefer update_memory for corrections, indicating when not to use forget and providing an alternative. Implicitly guides when to use: for destructive deletion or auto-forgetting.
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?
Despite no annotations, description discloses atomicity, preservation of memory_id, and referential integrity. It mentions caveat-layer constraints. Missing details on permissions or error behaviors, but adequate 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences. First sentence states core purpose and key benefit. Second sentence provides use cases and a constraint. No redundancy or unnecessary details.
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?
Covers purpose, usage, parameter details, and key behavioral traits (atomicity, referential integrity, caveat-layer limitation). Without output schema, return value is unmentioned, but overall completeness is high given the tool's complexity.
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 100%, but description adds value beyond schema: 'aliases accepted' for layer and 'Set to 0.9 or higher to pin' for importance. This enhances understanding beyond parameter names and types.
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 it atomically edits an existing memory in-place, specifying what actions it performs (update content, layer, importance). It explicitly distinguishes itself from the forget+remember alternative by highlighting preservation of memory_id and referential integrity.
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?
Provides explicit guidance on when to use: for correcting facts, updating deadlines, refining caveats, re-scoring importance. It also states a limitation for caveat-layer memories regarding protected flags, implying when not to fully update.
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?
Even without annotations, the description fully discloses behavioral traits: memory is stored in one of six layers, importance can pin a memory, and pasted output is rejected unless force=true. All behavioral aspects are transparent beyond what the schema provides.
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, well-structured paragraph that front-loads the core purpose, then details layers and usage. Every sentence adds value without redundancy.
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 the tool's complexity (7 params, 4 required, 6 layers, no annotations, no output schema), the description covers purpose, usage, layer semantics, quality check, and force flag. It is complete enough for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 86% schema coverage, the description adds significant meaning: explains each layer with its purpose (WHY, WHY-THIS-NOW, USER tone, HOW, PAIN lesson, GROWTH log), clarifies the importance field for pinning, and explains the force field bypassing quality checks. This goes beyond the schema descriptions.
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 stores a memory about an entity, specifies six layers (goal, context, emotion, implementation, caveat, learning), and distinguishes itself from sibling tools like recall, consolidate, forget, etc. The verb 'Store a memory' is specific and the resource (entity with layers) is well-defined.
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?
Explicitly states when to use ('discover non-obvious goals, unexpected failures, user preferences, or decisions worth preserving') and when not to use ('pasted assistant output or CI logs are rejected'), with an alternative (force=true). Provides clear context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![linksee-memory MCP server](https://glama.ai/mcp/servers/michielinksee/linksee-memory/badges/card.svg)](https://glama.ai/mcp/servers/michielinksee/linksee-memory)

Score Badge

Copy to your README.md:

[![linksee-memory MCP server](https://glama.ai/mcp/servers/michielinksee/linksee-memory/badges/score.svg)](https://glama.ai/mcp/servers/michielinksee/linksee-memory)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/michielinksee/linksee-memory'

If you have feedback or need assistance with the MCP directory API, please join our Discord server