mem-context

by turbyho

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.1.3
Disambiguation4/5
Most tools have clear distinct purposes like recall, remember, get, status. However, there is notable overlap between forget, delete_memory (soft-delete), and purge_memories, which all deal with removing or archiving memories, potentially causing confusion.
Naming Consistency3/5
Naming conventions are mixed: some are single verbs (forget, recall, update), some are verb_noun (delete_memory, purge_memories), and one is a noun phrase (consolidation_candidates). This inconsistency may reduce predictability.
Tool Count5/5
10 tools is well-scoped for a memory management server, covering creation, retrieval, search, updates, deletion, status, and consolidation. Each tool serves a necessary function without being excessive.
Completeness4/5
The toolset covers the full CRUD lifecycle plus consolidation and review. One minor gap is the lack of a direct 'list all memories' tool, though recall can approximate it with a broad query. Overall, no significant missing operations.
Average 4.7/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 77 commits in the last 12 weeks
- Last stable release on June 11, 2026
- 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.
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.
Add a glama.json file to provide metadata about your server.
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.
Add related servers to improve discoverability.

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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: three phases with timings, division of labor (server prepares, host reasons), and that it does not persist. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with bullet points for phases and return keys, front-loaded with the main purpose. Could be slightly tighter but every sentence adds value.
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 complexity, no output schema, and no annotations, the description covers inputs, phases, return dict structure, and interaction with the host model. Fully 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?
Schema coverage is 100% with a description for the sole parameter 'scope'. The description repeats 'Scope to consolidate. Auto-detected if omitted.' adding minimal value beyond the schema. The overall context helps but not directly for parameter semantics.
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 finds memories due for consolidation across a 3-phase pipeline, with specific phases and return types. It distinguishes from sibling tools like remember/update/forget by emphasizing it is read-only and only returns candidates.
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 says 'Read-only: yes. Only returns candidates. The host model must explicitly call remember/update/forget to persist.' This tells when to use (to get candidates) and when not (for writing results).
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?
Explains behavior: weight set to zero, remains in DB but excluded from recall results. States it's not read-only, mutates weight, reversible via update(). No annotations provided, so description carries full burden and does well.
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?
Front-loaded purpose, well-structured with sections. Some redundancy in Args vs schema, but overall efficient and clear.
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?
With one parameter, no output schema, and no annotations, the description covers purpose, behavior, reversibility, return value, and usage alternatives. Complete for agent usage.
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%, and the description repeats the parameter info from schema. Adds no new meaning beyond schema's 'Memory ID to archive (returned by remember() or recall()).' Baseline 3 applies.
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 'Archive a memory by setting its weight to zero.' It specifies the verb (archive), resource (memory), and mechanism. It distinguishes from siblings like delete_memory and purge_memories.
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 says 'Use for outdated or incorrect memories.' Provides alternatives for permanent deletion and bulk cleanup, guiding 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description thoroughly discloses behavior: read-only, no side effects, potential non-blocking background consolidation. It also explains the scoring factors. Given no annotations, the description fully carries the burden of 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 well-structured, front-loading the core purpose and usage. Every sentence is informative, though slightly verbose in parameter descriptions which are also in the schema.
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 6 parameters, an output schema described in text, and no annotations, the description is comprehensive. It covers purpose, behavior, parameter details, return format, and potential background effects, enabling an agent to use it correctly.
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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by providing optimal query length (5-15 words), score thresholds for applicability, and details on auto-detection for scope.
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 searches memories using vector search and six-factor scoring, and identifies itself as the primary read path. This distinguishes it from sibling tools like get (direct retrieval) and remember (storage).
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 calling the tool before every non-trivial response, providing strong usage guidance. However, it does not explicitly state when not to use it or directly compare with alternatives.
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 description carries full burden. Clearly states deletion is permanent and irreversible, uses strong language ('irreversibly', 'permanently'), and emphasizes safety with dry_run.
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?
Well-structured with overview, safety warnings, parameter list, and return format. No fluff; every sentence contributes essential 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 (destructive bulk delete with multiple filters), the description is comprehensive. Covers purpose, usage, parameters, return values, and safety. No output schema but return values are described.
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 coverage is 100% (baseline 3). Description adds value by listing parameter examples and constraints: e.g., type values 'episodic', 'semantic', 'permanent'; emphasizing dry_run for preview. Improves over schema alone.
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 is a selective bulk delete of memories by type, scope, or age. Distinguishes from siblings like delete_memory (likely single deletion) and forget (different mechanism). Verb 'purge' is specific.
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 instructs to use dry_run=True first and warns that deletion is irreversible. Notes that at least one filter is required to prevent full-table deletion. Does not explicitly compare to siblings but context 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, the description carries full burden and thoroughly discloses behaviors: write operation, creates or updates rows, triggers non-blocking background consolidation, deduplication at cosine similarity > 0.82, embedding generation via Ollama or local model, and decay rates per memory type.
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 comprehensive but well-structured, with clear sections and front-loaded purpose. It contains valuable details, though slightly verbose; still, every sentence adds meaning and earns its place.
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 10 parameters and no output schema, the description explains the return dict and covers complex behaviors like dedup, consolidation, and embedding. It provides enough context for an agent to invoke correctly without gaps.
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?
Schema coverage is 100%, but the description adds significant value beyond schema: details on embedding model (Ollama 1024d, fallback 384d), dedup threshold, decay rates, auto-summarization of first 200 chars, and default behaviors for optional 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 clearly states it stores a new memory with automatic embedding and deduplication, and specifies it's a write path for significant decisions, tricky bug fixes, or new pattern discovery. It effectively distinguishes from sibling tools like recall (read) and delete_memory.
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: after significant decisions, bug fixes, or pattern discovery. It implies its write-only nature but does not explicitly state when not to use, though the context is clear given sibling tools. The auto-dedup and background consolidation notes guide usage.
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?
Explicitly states 'Read-only: yes. No side effects', fully disclosing behavioral traits in the absence of annotations. It also mentions the return may be empty, adding 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?
Concise, front-loaded with the core purpose, followed by usage guidance, behavioral note, and return format. Every sentence is informative and no fluff.
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 zero parameters and an output schema (implied), the description fully covers what the tool does, when to use it, its side-effect-free nature, and the return structure, making it 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?
With no parameters and 100% schema coverage, the description adds value by explaining the return type and behavior, though no parameter details are 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 uses specific verb 'Return' and resource 'memories flagged for review', clearly stating the tool's purpose. It distinguishes itself from siblings like 'get' (retrieve specific) and 'recall' by focusing on flagged items.
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 advises 'Use this to surface issues that need human attention' and 'Call periodically to catch data quality issues', providing clear usage context but lacking explicit exclusions or alternatives.
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 were provided, but the description thoroughly discloses the tool's mutational behavior, irreversibility of hard delete, and the effect of both modes (setting weight=0 vs removing row). No contradiction with any structured data.
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?
Well-structured with clear sections (overview, modes, args, returns). Front-loaded with purpose and key distinction. Could trim minor redundancy (e.g., 'Read-only: no' is implied by 'mutates or removes a row') but overall efficient.
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 only 2 parameters and no output schema, the description fully covers the tool: explains modes, parameters with defaults, return format, and usage guidance. No gaps remain for an agent to invoke correctly.
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 coverage is 100%, so baseline is 3. The description adds value by explaining the default behavior of 'permanent', the reversibility note for soft-delete, and the context for each parameter beyond the schema comments.
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 deletes a memory with two modes (soft/hard), clearly identifying the verb and resource. It distinguishes itself from sibling tools like 'forget' and 'purge_memories' by detailing the deletion behavior and reversibility.
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 each mode: prefers 'forget' for routine archiving and reserves permanent=True for data that must be erased. Also notes reversibility of soft-delete.
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 fully discloses behavior: it mutates the memory row in LanceDB, lists allowed and blocked fields, states that blocked fields are silently ignored, and describes the return value including error case ('not found'). This is comprehensive.
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 well-structured into sections (purpose, constraints, behavior, args, returns). Every sentence adds value, and there is no redundancy or fluff. It is concise yet informative.
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 no output schema, the description explains the return structure (updated memory object or error). It fully covers the id and fields parameters, lists allowed values, and addresses error handling. No gaps remain for a mutation tool with these parameters.
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?
Schema description coverage is 100%, but the description adds significant meaning beyond the schema by explicitly listing allowed and blocked fields for the 'fields' parameter. This clarifies permissible values and constraints not evident from the schema's additionalProperties: true.
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 specifies the tool updates memory metadata including tags, weight, needs_review flag. It distinguishes from sibling tools by mentioning it is used 'after consolidation or to manually adjust a memory's importance.' The verb 'update' and resource 'memory metadata' are specific.
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 provides context on when to use ('after consolidation or to manually adjust a memory's importance') and explicitly states what cannot be done ('Cannot change the embedding vector or id'). However, it does not explicitly contrast with sibling tools like 'delete_memory' or 'forget' for when not to use.
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 behavior: read-only, no side effects, and returns a dict with specific fields. It also explains the scope parameter's effect. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: summary, behavioral note, usage advice, parameter section, return section. 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 no output schema, the description details the return dict schema. It covers parameter usage, behavior, and use cases. Fully informs an agent on how and when to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema describes the 'scope' parameter. The description adds value by explaining usage ('Returns global stats if omitted') and linking to use cases (before purge). This provides context beyond the schema's description.
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 returns memory store statistics with a specific verb ('Return'). It distinguishes from siblings like 'get' or 'recall' by emphasizing aggregate stats rather than individual memories. The read-only and no-side-effects affirmations further clarify its non-destructive nature.
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 advises when to call the tool: 'at session start to check if relevant context exists, or before purge to confirm scope contents.' This provides clear guidance versus alternatives and implies not to use for specific memory retrieval.
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?
Without annotations, the description fully discloses behavior: declares 'Read-only: yes,' details what the return dict includes and excludes (e.g., embedding excluded), and specifies None if not found.
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 concise and well-structured with a clear purpose line, read-only note, usage contexts, args section, and returns section. Every sentence adds value.
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 simple tool with one parameter, explicit return structure, and no output schema needed, the description is complete and covers all necessary context.
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?
The only parameter 'id' is described in the schema and the description adds value by stating it is 'returned by remember(), recall(), or consolidation,' which enriches the schema's description.
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 'Retrieve a single memory by its ID with all metadata,' specifying the verb (retrieve) and resource (memory by ID). This distinguishes it from sibling tools like recall (which likely returns multiple memories) and update.
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: 'Use to inspect a memory returned by recall() or to verify the state of a memory after update().' This guides the agent on appropriate contexts and implies not for listing.
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:

[![mem-context MCP server](https://glama.ai/mcp/servers/turbyho/mem-context/badges/card.svg)](https://glama.ai/mcp/servers/turbyho/mem-context)

Score Badge

Copy to your README.md:

[![mem-context MCP server](https://glama.ai/mcp/servers/turbyho/mem-context/badges/score.svg)](https://glama.ai/mcp/servers/turbyho/mem-context)

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/turbyho/mem-context'

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