Mnemex

by fastmcp-me

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.5.6
Disambiguation4/5
Tools are mostly distinct, with clear separation between search, retrieval, mutation, and maintenance operations. The main potential confusion is between search_memory and search_unified, and between cluster_memories and consolidate_memories, but descriptions clarify their different scopes.
Naming Consistency4/5
Nearly all tools follow a consistent verb_noun snake_case pattern (e.g., save_memory, search_memory, consolidate_memories). The abbreviation 'gc' for garbage collection is the only notable deviation, making the naming slightly inconsistent.
Tool Count5/5
The 13 tools are well-scoped for a memory server, covering creation, retrieval, search, maintenance, relations, and metrics. Each tool has a clear role without redundancy or excessive fragmentation.
Completeness4/5
The tool set covers the core memory lifecycle: creation (save_memory, create_relation), retrieval (open_memories, search_memory, read_graph), reinforcement (touch_memory), consolidation (cluster_memories, consolidate_memories), promotion (promote_memory), and deletion (gc). Minor gaps include lack of direct memory content update and relation deletion, but these are workable via existing tools.
Average 4.4/5 across 13 of 13 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It simply says 'Get current performance metrics and statistics' without disclosing side effects, resource usage, or whether it requires a prior setup. No behavioral traits are disclosed beyond the obvious read-only nature of 'get'.
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 very short, consisting of two sentences, and gets to the point immediately. However, 'performance metrics' and 'performance statistics' are slightly redundant, but overall it is appropriately concise.
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 are zero parameters and an output schema exists, the description sufficiently covers the tool's purpose. It doesn't elaborate on the exact metrics included, but that is presumably handled by the output schema.
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?
This tool takes no parameters, and the schema confirms this with an empty properties object. The baseline score of 4 is appropriate since there is nothing for the description to add.
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 'Get current performance metrics and statistics', indicating a read-only retrieval operation. This contrasts with sibling tools like reset_performance_metrics, making the purpose unambiguous.
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 information is provided about when to use this tool versus alternatives. There is no mention of when it's appropriate to call this, or that reset_performance_metrics is for clearing metrics.
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?
With no annotations, the description must disclose behavior itself. It states that all performance metrics are reset and that confirmation is returned, but it does not warn that the action is irreversible or describe any side effects. This is minimal but not misleading.
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 very concise and front-loaded, with no filler. However, the 'Returns:' line is likely redundant if an output schema is present, so it does not earn full marks for economy.
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 zero-parameter reset tool with an output schema, the description gives the core action and result. It lacks an explicit caution about destructiveness or a reference to the read-only sibling, but it is sufficiently complete for a simple reset operation.
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 tool has zero parameters, so there is no parameter information to explain. The baseline of 4 applies because the description does not need to compensate for undocumented 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 uses a specific verb ('Reset') and names the precise resource ('all performance metrics'), clearly distinguishing it from the read-only sibling get_performance_metrics. It also states the return behavior, making the tool's purpose unambiguous.
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 given about when to use this tool versus alternatives. It does not mention that it is the destructive counterpart to get_performance_metrics or suggest checking metrics before resetting, so the context for appropriate use is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return value ('Created relation ID and confirmation') and error behavior (ValueError on invalid input), which adds transparency beyond the mere schema. However, it does not mention side effects such as whether existing relations are updated or if the operation is reversible, leaving behavioral gaps 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with Args, Returns, Raises, making it scannable. The opening sentence and the second sentence are nearly redundant ('Create an explicit relation' vs. 'Links two memories'), but the overall length is appropriate and information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has five parameters and no annotations, so the description must carry the load. It covers parameter constraints and errors, but glosses over the meaning of each relation_type and the implications of creating a relation (e.g., whether the memory graph is modified in place). This leaves some contextual gaps for agent decision-making.
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 zero schema description coverage, the description compensates well by detailing each parameter: from_memory_id and to_memory_id are 'valid UUID', relation_type lists all allowed values, strength is a range (0.0-1.0), and metadata is 'additional metadata'. It does not specify the metadata structure or strength default, but the schema already covers the default.
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 function: 'Create an explicit relation between two memories.' This is a specific verb ('create') and resource ('relation between two memories'), distinguishing it from sibling tools like read_graph or search_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 conveys when to use the tool: when the agent needs to link two memories with a typed relationship. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous enough to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses several behavioral traits: the 'Maslow effect' blending of review candidates, pagination mechanics, and the possibility of review candidates in results. It also mentions ValueError on validation failure. It doesn't explicitly state whether the operation is read-only or modifies scheduling state, but it provides more detail than most.
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-organized with clear sections (pagination, args, returns, examples, raises) and front-loaded with a one-line summary. It is somewhat verbose and repeats the page/page_size explanation in both the pagination paragraph and the Args section, but the structure and examples keep it usable.
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 tool with 9 parameters, no annotations, and an output schema, the description covers the essential aspects: parameter semantics, pagination, return structure, examples, and error behavior. It could better address when to prefer this over search_unified or clarify side effects, but it is largely complete.
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 Args section describes every parameter with meaningful constraints and defaults, e.g., 'query: Text query to search for (max 50,000 chars)' and 'page_size: Number of memories per page (default: 10, max: 100).' Since the schema provides zero parameter descriptions, this fully compensates.
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 it searches memories with optional filters and scoring, and explains the unique spaced repetition behavior. However, it doesn't distinguish itself from the sibling tool 'search_unified', so it falls short of a 5.
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 provides usage guidance for pagination ('If a search term isn't found on the first page, increment page to see more results') and explains the review-candidate blending. But it never says when to use this tool over alternatives like search_unified, and there are no exclusions.
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 are provided, so the description carries the full burden and does well: it explains side effects (resets temporal decay, increases resistance to forgetting, optionally boosts base strength) and documents return values and errors. Minor gap: no mention of permission requirements or reversibility, but the operation is simple.
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 clear sections (overview, Args, Returns, Raises). Every sentence provides necessary information 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?
For a low-complexity tool with an output schema, the description covers behavior, parameters, return values, and error conditions. It is self-sufficient despite missing annotations, making it complete enough 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 description coverage is 0%, so the description's Args section compensates by explaining memory_id as a valid UUID and boost_strength as a boolean toggle. This adds meaning beyond the raw schema, though the default value of boost_strength is not repeated.
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 ('Reinforce a memory') and the specific mechanism ('updating its last accessed time and use count'), distinguishing it from sibling tools like promote_memory. It also mentions optional boost behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit when-to-use guidance or compare against alternatives. It implies usage through the verb 'reinforce' but lacks context about situations where this tool is preferred over siblings like consolidate_memories or open_memories.
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?
With no annotations, the description must fully disclose side effects. It mentions permanence, dry_run preview, force override, and storage format details. However, it does not state whether the original memory is moved or copied, or whether the operation is destructive to the source, which is a significant transparency gap.
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 with a front-loaded purpose, followed by a clear Args section that adds essential context. It also includes Returns and Raises sections. Every sentence contributes useful information without redundancy.
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?
The description explains the return format and error cases, and the parameter details are thorough. It only lacks explicit clarification on mutual exclusivity of memory_id and auto_detect, and the exact promotion criteria threshold, which would make it fully complete for a tool with this complexity.
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 0%, but the description compensates fully by explaining each parameter: memory_id (valid UUID), auto_detect (candidate detection), dry_run (preview), target (storage backend and format note), and force (override criteria). This adds substantial meaning beyond the bare schema.
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 a specific verb 'promote' with resource 'memories to long-term storage' and clarifies the high-value criterion. It clearly distinguishes from sibling operations like save_memory by focusing on moving high-value memories to permanent 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 gives clear context on when to use this tool (to make high-score or frequently-used memories permanent) and explains parameters that affect usage (memory_id vs auto_detect, dry_run for preview). However, it does not explicitly name alternatives or state when not to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes on the full burden of behavioral disclosure. It transparently explains key side effects such as temporal decay, automatic forgetting if unused, and potential promotion to long-term storage. It also documents the ValueError on validation failure, providing meaningful behavioral context beyond the schema.
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 sized, with an introductory sentence explaining storage behavior followed by an Args list. Each sentence provides useful information, and the structured list is easy to scan. It could be slightly tighter, but there is no filler or redundancy.
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 the tool's moderate complexity and the absence of annotations, the description covers the essential aspects: purpose, behavioral consequences, parameter semantics, and possible errors. An output schema exists, so return values need not be explained. It could mention interactions with siblings, but that is not strictly required for correct 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?
Schema description coverage is 0%, so the description must compensate. The Args section adds valuable semantic meaning to every parameter: content, tags, entities, source, context, and meta all receive explanatory text plus constraints like character limits and counts. This far exceeds the raw type information in the schema.
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 opens with 'Save a new memory to short-term storage,' which clearly states the action (save) and resource (memory). It further distinguishes from siblings by noting temporal decay and auto-promotion, setting it apart from 'promote_memory' and 'consolidate_memories'.
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 implies when to use the tool—creating a new memory—and contrasts with sibling operations like searching or consolidating through its focus on saving. It does not explicitly state exclusions or alternatives, but the context is clear enough for an agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains pagination behavior, the need to increment 'page' when results are not found on the first page, and the exact return structure (results + pagination metadata). It also states validation errors (ValueError). While it doesn't explicitly mention read-only or side-effect-free behavior, that is inherent in a search operation and does not detract significantly.
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 with clear sections for pagination, arguments, return values, examples, and exceptions. It is detailed but every sentence serves a purpose; the examples and parameter constraints are essential given the schema's lack of descriptions. The front-loaded purpose and logical organization make it easy to parse, and no content feels redundant.
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 (9 parameters, no annotations), the description is exceptionally complete. It covers all parameters, pagination flow, return format, error handling, and provides concrete usage examples. Even though an output schema exists, the description's return summary adds context about pagination metadata. This is a well-rounded, self-sufficient description.
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 input schema has zero description coverage, so the description must fully compensate. The 'Args:' section provides comprehensive, semantics-rich explanations for all 9 parameters, including constraints (max query chars, max tags, numeric ranges, defaults) and the meaning of each weight/filter. This goes far beyond the bare schema and is exactly what an agent needs to invoke the tool correctly.
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 opens with a clear, specific action: 'Search across both STM and LTM with unified ranking.' This verb+resource combination immediately distinguishes it from sibling tools like search_memory, which likely targets a single memory store. The phrasing is neither tautological nor vague, and it sets accurate expectations for a cross-memory search.
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 clearly implies usage for combined STM/LTM search and provides detailed pagination guidance, including how to navigate large result sets and retrieve subsequent pages. However, it does not explicitly name alternatives or state when NOT to use this tool (e.g., when only one memory store is needed). This clear context without exclusions aligns with a score of 4.
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 the full burden. It discloses that clustering uses 'semantic similarity (if embeddings are enabled) or other strategies,' explains parameter constraints and config defaults, describes the return value ('List of clusters or duplicate pairs with scores and suggested actions'), and notes the exception ('ValueError: If any input fails validation'). This gives a comprehensive behavioral picture.
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 and front-loaded: a one-sentence summary, a short explanatory paragraph, then a clear Args/Returns/Raises breakdown. Each section is informative without redundancy, and the formatting uses standard docstring conventions.
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 five parameters, no annotations, and an output schema (but not shown here), the description provides essential context: purpose, parameter semantics, return type, and error behavior. It is sufficiently detailed for an agent to invoke the tool correctly and understand the outcome.
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 0%, but the Args section fully compensates by explaining every parameter: strategy, threshold (0.0-1.0, config default), max_cluster_size (1-100), find_duplicates (boolean), and duplicate_threshold (0.0-1.0). This adds meaningful constraints and defaults beyond the schema.
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 function: 'Cluster similar memories for potential consolidation or find duplicates.' It specifies the verb (cluster/find), resource (memories), and differentiates from siblings like consolidate_memories by noting it is for 'potential consolidation' and 'identifying redundant memories.'
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 for when to use the tool: 'Useful for identifying redundant memories' and 'for potential consolidation.' It implies this is a preparatory step before consolidate_memories, but does not explicitly name alternatives or state when not to use it. This is clear context without explicit exclusionary guidance.
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 carries the transparency burden. It discloses the merging algorithm's behavior: preserving unique content, unioning tags, calculating strength, and preserving earliest/latest timestamps. It clearly states that preview makes no changes while apply executes, and it documents error conditions (ValueError) for invalid inputs, offering complete behavioral 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 well-organized with a clear introductory sentence, numbered behavioral steps, a mode breakdown, an args list, and returns/raises sections. Every sentence contributes meaning, and the structure makes it easy to scan. It is appropriately detailed for a tool with four parameters and multiple modes.
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, auto-detection, thresholds, multiple merge behaviors), the description covers all essential aspects: what it does, how to invoke each mode, parameter details, expected returns, and error handling. The output schema existence is acknowledged, and the description's return summary complements it. No gaps are evident.
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 schema provides zero parameter descriptions (0% coverage), but the description compensates with detailed semantics: cluster_id is a valid UUID required for apply mode, mode is 'preview' or 'apply', auto_detect triggers high-cohesion detection, and cohesion_threshold has a 0.0-1.0 range with default 0.75. This fully explains what each parameter means and how it influences behavior.
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 opens with 'Consolidate similar memories using algorithmic merging,' a specific verb-object pair that clearly captures the tool's function. It distinguishes itself from sibling tools like cluster_memories by focusing on merging and consolidation rather than grouping or detection. The description also enumerates the concrete actions performed (combining content, merging tags, etc.), reinforcing 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?
The description provides explicit usage context for modes: 'preview' generates a merge preview without changes, while 'apply' requires a cluster_id. It explains auto_detect and cohesion_threshold for automatic cluster selection, giving clear scenario-based guidance. However, it does not explicitly compare to sibling tools like cluster_memories or state when not to use this tool, leaving some room for ambiguity.
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 carries the full burden of behavioral disclosure. It clearly explains that the tool deletes or archives memories, supports a dry-run preview, and raises ValueError for invalid limits, providing key behavioral details.
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 with a one-sentence summary followed by Args/Returns/Raises sections. Every sentence provides useful information and the placement is front-loaded.
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 has an output schema and moderate complexity, the description covers operation thoroughly: purpose, parameters, return statistics, and errors. It is complete for an agent to select and invoke the tool correctly.
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 0%, but the Args section fully explains each parameter: dry_run previews without removing, archive_instead changes deletion to archival, and limit constrains the count with a range. This adds significant meaning beyond the schema's bare types and defaults.
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 performs garbage collection on low-scoring memories, with a specific verb and resource. It further explains it removes or archives memories below a decay threshold, distinguishing it from sibling memory manipulation tools.
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 clear context for when to use the tool: to prevent the database from growing indefinitely with unused memories. However, it does not explicitly name alternative tools or state when not to use it, so it lacks the full when/when-not guidance.
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 carries the full burden, and it delivers substantial detail: pagination behavior, maximum of 100 IDs, error raises (ValueError), not_found list in the return, and options to include relations/scores. It explains the structure of the return dictionary including pagination metadata. This well exceeds typical 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 well-structured with separate sections for summary, pagination note, args, returns, examples, and raises. Each section is informative and non-redundant, and the length is justified given the 5 parameters and pagination complexity. It is front-loaded with the primary purpose.
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?
The tool is moderately complex with pagination, multiple optional flags, and error handling, and the description covers all of these: parameter meanings, return structure, error conditions, and usage examples. It even includes a maximum ID count and pagination navigation examples. Given the output schema exists, the return structure section adds extra context without being required.
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 0%, but the description thoroughly compensates by explaining each parameter with syntax, constraints, and defaults: memory_ids accepts single or list up to 100, page is 1-indexed with default 1, page_size max 100, include_scores means decay scores and age, and include_relations means relations from/to. It also clarifies the schema's null defaults by specifying actual defaults.
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 opens with 'Retrieve specific memories by their IDs,' which clearly identifies the verb, resource, and retrieval method. This distinguishes it from sibling tools like search_memory (query-based) and read_graph (graph traversal). The reference to the MCP memory server's open_nodes functionality adds further context.
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 establishes that this tool is for retrieving memories when their IDs are known, which is clear usage context. However, it does not explicitly exclude alternatives or name sibling tools for contrasting cases, such as using search_memory when IDs are unknown. The 'Similar to open_nodes' reference provides orientation but no direct when-to-use vs. when-not-to-use guidance.
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 carries the full burden of behavioral disclosure, and it excels. It discloses that results are paginated, that relations are not paginated while memories are, that decay scores require include_scores, and that invalid inputs raise ValueError. It also details the exact return dictionary structure, making behavior predictable.
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 and front-loaded: purpose first, then return summary, pagination, detailed args, return schema, examples, and errors. It is long but every section earns its place; there is no redundancy or fluff. The use of headings and examples improves scannability and comprehension.
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 (paginated graph reading, multiple parameters, rich output), the description covers all necessary aspects: purpose, parameters, return structure, pagination behavior, error conditions, and examples. The presence of an output schema and this description together fully equip the agent to use the tool correctly without additional assumptions.
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 input schema has zero description coverage (only titles and defaults), so the description's Args section is essential. It explains each parameter's role, allowed values (e.g., status filter values), constraints (limit 1-10,000, page_size max 100), and defaults. It also provides concrete examples that illustrate parameter usage, adding significant meaning beyond the schema.
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 opens with a specific verb and resource: 'Read the entire knowledge graph of memories and relations.' It clearly distinguishes the tool from siblings like search_memory or cluster_memories by stating its function is to retrieve the complete graph structure, not to filter or analyze. The explanation of return content (memories, relations, statistics) further clarifies its 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?
The description provides clear context for when to use the tool: to read the whole graph, with pagination guidance and examples for navigating results. However, it lacks explicit comparison to alternatives such as search_memory or search_unified, so it does not fully reach the 'when not to use' criterion. The pagination advice ('If searching for specific memories or patterns, increment page') is a bit conflated with searching but still offers practical usage direction.
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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