Engram Alpha

Server Quality Checklist

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Latest release: v2.1.0
Disambiguation4/5
Most tools have distinct targets: memory nodes, graph relations, ingestion, maintenance, and stats. A few pairs like save_memory vs extract_and_save_memory or search_memory vs auto_context could overlap, but their descriptions clarify the intended workflow. No tools are truly redundant.
Naming Consistency4/5
Tool names are consistently lowercase snake_case and mostly follow a verb-first pattern. The main outlier is auto_context, which is a noun phrase, and graph-related names mix save_graph_relation, query_graph, and visualize_graph. Overall the pattern remains predictable and readable.
Tool Count5/5
15 tools is at the upper edge of ideal but well-scoped for a memory system that combines vector storage, knowledge graph operations, ingestion, reflection, and maintenance. Each tool serves a distinct purpose, and none feel purely decorative or redundant.
Completeness4/5
Memory CRUD is well covered with save, search, edit, delete, and list, and graph relations have save and query support. Missing direct graph relation editing/deletion and a get-memory-by-ID tool are minor gaps, but cascade deletion and search/list provide workarounds.
Average 3.3/5 across 15 of 15 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 76 commits in the last 12 weeks
- Last stable release on September 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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 provided, the description must disclose side effects and invocation consequences. It reveals the internal pipeline (LLM sidecar, regex heuristics, vector indexing) but omits that this is a mutating/write operation, whether graph triples are persisted, and any effects on existing memories. The word 'Autonomous' also hints at behavior without explaining its scope.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The text is reasonably short, but the 'Autonomous Agent:' label and marketing-style wording waste the opening. The implementation details (local LLM sidecar, expanded NLP regex heuristics) add flavor rather than directly actionable guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with five parameters, no annotation coverage, and a write-oriented workflow, the description is incomplete. It does not explain when parameter overrides matter, what persistence side effects occur, or how this complements related memory tools. The output schema exists, but the description still leaves key invocation context missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only implies that 'text' is the content to be deconstructed. It adds no meaning for agent, project, category, or importance, so an agent cannot determine how those optional parameters affect extraction or storage.
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 names a specific process: deconstructing text into atomic facts, extracting entity triples, and indexing vectors. It conveys that this is an extraction-focused memory tool, though it does not explicitly say the results are persisted and does not distinguish it from siblings like save_memory or save_graph_relation.
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?
There is no guidance on when to choose extract_and_save_memory over alternatives. The sibling list includes save_memory, save_graph_relation, deduplicate_memories, and consolidate_reflections, but the description never names them or states the conditions that favor this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Synthesizes ... into durable insights' implies a transformation, but the description does not disclose whether source episodic nodes are deleted, archived, or preserved, nor whether the tool runs autonomously or requires user intervention. This ambiguity is significant for a memory-modifying operation.
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 compact and front-loaded with the core action. The subtitle-like prefix 'Autonomous Memory Reflector Agent (Episodic Reflection)' adds mild context without much bloat. It earns a high score for efficiency, though it sacrifices informative detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a consolidation tool with no annotations and an output schema that likely only describes return values, the description leaves critical context missing: side effects, when to run, and how this differs from deduplication or saving. The simplicity of two parameters does not compensate for the lack of behavioral or usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no detail about 'topic' or 'project.' The parameter names are somewhat self-explanatory, but 'project' with a default of 'default' is unexplained and could mean a namespace, workspace, or grouping. The description fails to compensate for the missing schema descriptions.
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 states a specific verb and object: 'Synthesizes low-level episodic nodes into durable high-level insights.' This clearly distinguishes it from memory search, save, and graph tools, though it does not explicitly contrast with similar consolidation tools like deduplicate_memories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to invoke consolidate_reflections versus searching, saving, or deduplicating memories. The description does not mention prerequisites, conditions, or scenarios where consolidation is appropriate, leaving the agent to infer usage from the title alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions bi-temporal validity and project namespace, but does not explain side effects, overwrite semantics, validation behavior, or what happens to superseded relations. This is insufficient for a mutating 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 a single tight sentence with no filler or redundancy. Key concepts are front-loaded. It is concise, though the dense phrase 'bi-temporal validity' could have been expanded without hurting clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 8 parameters, no annotations, and no schema descriptions, this one-sentence description is not complete. It lacks usage context, parameter semantics, and behavior expectations. The presence of an output schema reduces the need to describe return values, but the other gaps remain significant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate for all parameter explanations. It maps source, relation, and target to the SPO model and references project and bi-temporal fields, but it does not explain weight, valid_from/valid_until formats, or the semantics of superseded_by. Critically, none of the 8 parameters have meaningful descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool saves a Subject-Predicate-Object relation, which is a specific verb and resource. It also adds distinguishing details about bi-temporal validity and project namespace. However, it does not explicitly contrast it with sibling tools like save_memory or query_graph.
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?
There is no guidance on when to use this tool versus alternatives such as save_memory, extract_and_save_memory, or query_graph. The description does not mention prerequisites, when not to use it, or how it differs from related save/query tools.
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 carry behavioral disclosure. It conveys that the operation is a query (read-oriented) and describes traversal and temporal filtering behaviors. However, it does not mention side effects, permission needs, output shape, or any constraints, leaving some transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It is concise and includes the core action plus key modifiers, though the term 'bi-temporal filtering' is dense and might obscure meaning for some agents.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four parameters with zero schema descriptions, no annotations, and sibling alternatives, the description is too sparse for correct invocation. It omits parameter meanings and usage context; the presence of an output schema only covers return values, not how to form a valid request.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
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 by explaining parameters. It does not: 'node', 'depth', 'project', and 'include_superseded' are left undefined, and the description's bi-temporal filtering reference only hints at one parameter without mapping.
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 ('Query') and resource ('knowledge graph relations') and names distinctive capabilities: 'recursive multi-hop path traversal' and 'bi-temporal filtering'. This clearly differentiates it from sibling tools like search_memory or visualize_graph.
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 provides no when-to-use guidance, no exclusions, and does not name any alternative tool or conditions for choosing this one over a sibling. The implied use case (graph traversal with temporal filtering) is not enough to route an agent reliably.
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 provided, the description carries the full burden of behavioral disclosure. It does reveal a significant behavioral trait—the search fuses dense vector, lexical FTS5, graph spreading activation, and ACT-R decay—but it does not mention side effects, read-only nature, result ordering, pagination, or the effect of the hybrid flag.
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 tight sentences with the primary verb and resource front-loaded. Every phrase earns its place, and the technical detail is packed efficiently into a colon-introduced list without filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the definition omits parameter semantics and usage guidance for a 4-parameter tool with 0% schema description coverage. An agent would be uncertain about the purpose of the hybrid toggle and the project filter, so the description is not complete enough for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needed to compensate, but it defines none of the four parameters. Query and limit are self-explanatory from their names, but hybrid is ambiguous given the tool itself is already described as hybrid, and project's filtering semantics are unspecified.
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 opens with a clear verb and resource ('Search memory') and specifies the distinctive 4-Way RRF fusion mechanism, making the tool's core function obvious. It does not explicitly distinguish itself from sibling tools like list_memories or query_graph, but the technical detail prevents it from being a generic or tautological description.
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 provides no guidance on when to use this tool versus its siblings, no exclusions, and no alternative recommendations. It focuses entirely on the internal search mechanism, leaving an agent to infer use cases from tool names alone.
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 carries the disclosure burden. It does disclose the primary behavior: generating Mermaid.js and ASCII relational network diagrams. However, it does not state whether the operation is read-only, what depth means, or how missing nodes are handled.
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 compact and front-loaded with the tool's purpose. The 'for power users' phrase adds marginal value, but overall the text is scannable and free of redundant schema repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters with zero schema-level descriptions and no annotations, the description is too thin for an agent to assemble a correct call. The output schema may cover return structure, but input semantics and usage context are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needed to explain node, depth, and project. It mentions none of them, leaving the required 'node' parameter ambiguous and the 'depth' semantics entirely unexplained.
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 a specific verb and resource: it generates Knowledge Graph topology diagrams. It also names concrete output formats (Mermaid.js and ASCII), which distinguishes it from sibling tools like query_graph or save_graph_relation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus query_graph or other graph-related siblings. The word 'Visualizer' implies use for diagramming, but there are no explicit when-to-use or when-not-to-use conditions.
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 carries the full behavioral burden. It does disclose sorting behavior and recency-based selection, but it does not explicitly state that this is a read-only operation or describe any side effects, auth requirements, or scoping limitations.
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 clear, front-loaded sentence with no filler. Every word contributes to the core behavior.
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?
This is a simple list tool with no required parameters and an output schema, so the description is minimally viable for a basic call. However, it is incomplete for correct advanced use: the project parameter is unexplained, and there is no guidance on choosing this tool over search_memory.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 for the limit and project parameters, but it does not mention either. An agent is left to guess what 'project' filters or whether 'limit' caps the number of returned nodes.
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 states the action (list), the resource (recent memory nodes), and a meaningful ordering rule (importance and recency). This makes the tool's basic behavior clear, though it does not explicitly distinguish it from the sibling search_memory tool.
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?
There is no guidance about when to use this tool versus search_memory or other siblings. No conditions, exclusions, or alternative tool names are mentioned, so an agent must infer the right context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It states the operations performed, but does not disclose side effects, whether vacuum alters data, potential performance impact, or any authorization requirements. This leaves an agent under-informed about the consequences of calling it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler or redundant phrasing. Every word contributes to the intended benefit.
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 is simple—no parameters and an output schema is present—so the description doesn't need to explain return values. However, it still lacks any guidance about when maintenance should be performed and what operational effects to expect, leaving it minimally complete but not fully informative.
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 and the schema already fully describes this fact. The baseline of 4 applies because there is no parameter semantics to add.
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 names concrete operations (WAL flush, vacuum, performance optimization) and clearly identifies this as a database maintenance tool, distinguishing it from memory/graph sibling tools. It does not explicitly state the resource, but the tool name plus these operations make the intent clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to run a checkpoint or how to decide between this tool and alternatives. There are no exclusions, prerequisites, or context signals to help an agent decide whether this maintenance action is appropriate at a given time.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full behavioral disclosure burden. It names the destination of the data but does not mention whether ingestion is idempotent, overwrites existing data, requires authentication, is long-running, or has side effects on previously uploaded content.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word contributes meaning, clearly communicating the action and destination.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the tool is a bulk ingestion operation with likely side effects, and the description omits critical operational details such as idempotency, failure behavior, and whether the process is incremental or full-reload. The 0% schema coverage and absent annotations leave significant gaps for an agent deciding to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate for unexplained parameters. It gives context for 'vault_path' by clarifying the vault is Obsidian markdown, but it completely ignores the 'project' parameter, leaving its purpose and effect ambiguous.
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 states a specific verb ('ingest'), a specific resource ('entire Obsidian markdown vault'), and clear targets ('knowledge graph and vector store'). This distinguishes it from siblings like save_memory or extract_and_save_memory, which handle individual items rather than bulk vault ingestion.
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?
Usage context is implied by the phrase 'entire Obsidian markdown vault' — it is for bulk import. However, the description does not explicitly state when to prefer this over alternatives, provide exclusions, or mention prerequisites such as vault structure or path requirements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure, but it only says 'edit' and does not clarify whether fields are updated selectively, whether null values clear fields, or what happens if the node ID does not exist. For a mutation tool, this leaves important edge-case behavior undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the operation and specifies the resource and fields. Every word is useful; there is no filler or repetition.
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?
For a simple mutation with an output schema, the core operation and input parameters are adequately described. Still, with zero annotations and zero schema descriptions, the definition leaves usage guidance and behavioral edge cases implicit rather than explicit.
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?
The description maps id to 'node ID' and names content, importance, and category as the editable fields, adding some meaning beyond the bare schema. However, it does not explain value semantics such as importance range, category format, or whether content replaces the previous value.
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 states a specific operation — edit an existing memory — and enumerates the mutable fields (content, importance, category) plus the locator (node ID). This clearly distinguishes it from siblings such as save_memory, delete_memory, and 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when you already have a memory's node ID and want to modify its fields. It does not explicitly say when not to use it or name save_memory as the alternative for creating a new memory.
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 provided, the description carries the burden of behavioral disclosure. It does indicate that the operation creates a new node, computes a dense vector embedding, and performs conflict detection. However, it does not explain what conflict detection actually does—whether it blocks the save, returns a warning, or triggers deduplication—and it omits any side-effect or authorization 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 entire description is one front-loaded sentence with no filler. It leads with the action and object, then lists relevant attributes efficiently. Every phrase adds either operational clarity or meaningful behavioral detail.
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 presence of an output schema reduces the need to explain return values. However, for a tool with no annotations and several sibling tools, the description leaves gaps: conflict detection behavior is unexplained, the agent parameter is ignored, and there is no guidance distinguishing direct saving from extract_and_save_memory. The core create operation is clear, but the surrounding context is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It names category, importance, and project namespace, which maps to three of the five parameters, and 'dense vector embedding' hints at how content is processed. However, it says nothing about the agent parameter and does not clarify what content should actually contain, leaving the required parameter semantically under-described.
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 states a specific action ('Save') on a specific resource ('a new memory node'), clearly distinguishing this from sibling operations like edit_memory, delete_memory, or save_graph_relation. The added details about category, importance, project namespace, and conflict detection make the tool's purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: call this when creating and persisting a new memory node. However, it provides no explicit guidance about when to prefer this over alternatives like extract_and_save_memory, edit_memory, or deduplicate_memories, and there are no exclusion criteria or prerequisites stated.
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?
There are no annotations, so the description carries the transparency burden. It usefully discloses that only top, high-importance, active memories are returned and that the output is XML. However, it does not state whether the tool has side effects, how 'active' is determined, or behavior when no memories match.
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 clear sentence with a useful label prefix. No filler words; the key behavior, filtering criteria, output format, and intended moment of use are all packed efficiently.
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?
For a read-like boot tool with an output schema and all optional parameters, the description is workable but incomplete. The output schema covers returns, yet the project parameter and the meaning of 'active' are not explained, so an agent has to infer important semantics from parameter names.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention limit, project, or min_importance. 'High-importance' and 'top' loosely map to min_importance and limit, but project is entirely unaddressed, so the description does not compensate for the lack of 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 states a distinct action ('Recalls top high-importance active memories') and a concrete output ('formatted in XML'), with a specific use case ('session initialization'). This distinguishes it from siblings like list_memories or search_memory by selection criteria and format, even though no sibling is named explicitly.
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?
It explicitly positions the tool as a boot/session-initialization tool, which tells the agent when to use it. It does not explicitly contrast it with alternatives or state when not to use it, so it stops 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, the description carries the full behavioral burden, and it is fairly informative: it discloses autonomous operation, merging of access counts and edges, and pruning/destruction of duplicate records. The chunk size of 1,000 nodes also reveals batching behavior. It could be more explicit about irreversibility or data-safety implications, but the core mutating behavior is clearly stated.
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 tightly written, front-loaded with the main purpose, and each clause adds new information: clustering, merging, pruning, and batching. No fluff or redundant repetition of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is an output schema and all parameters are optional with defaults, the description provides a workable picture of the tool's behavior. However, it lacks explicit usage guidance and detailed parameter semantics, and it does not warn about the autonomous, mutating nature beyond saying 'Autonomous' and 'prunes.' It is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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. It only indirectly addresses batch_size via 'chunks of 1,000 nodes' and hints at similarity_threshold via 'near-identical.' The project parameter is completely unexplained, and no guidance is given for thresholds, defaults, or interaction between 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 states a clear, specific purpose: finding duplicate/near-identical memory nodes, merging access counts and edges, and pruning duplicates. This goes beyond the tool name and gives the agent a concrete model of what the operation does, distinguishing it from memory read/write siblings.
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?
Usage context is implied: an agent would call this when memory deduplication is needed, and the description makes that obvious. However, it never explicitly says when to prefer this over consolidate_reflections or other maintenance tools, nor does it mention any conditions or alternatives.
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 behavioral burden. It explicitly discloses the cascade-delete side effect on associated knowledge graph edges, which is critical for a destructive operation. It doesn't mention reversibility or error behavior, but the core side-effect is 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?
One sentence, front-loaded with the primary action and immediately followed by the cascade side-effect. No filler.
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?
Description covers the action and key side-effect. The output schema exists, so return values are covered elsewhere. Missing are error-case behavior and prerequisites, but for a single-id delete tool, this is nearly 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 description coverage is 0%, so the description must compensate. It adds that the operation is 'by ID,' clarifying that the id parameter identifies the memory node. However, it doesn't explain the ID format, how to obtain it, or what happens when the ID is invalid. The parameter name and tool name already make the meaning mostly inferable.
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 states a specific verb ('Delete') and resource ('memory node'), and further specifies cascade-deletion of knowledge graph edges. This clearly distinguishes it from sibling tools like edit_memory or save_memory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It doesn't mention that edit_memory should be used for non-destructive changes, or that deletion is permanent. Usage is only implied by the name and verb.
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 must convey safety and behavior itself. The verb 'Get' clearly signals a read-only operation, and the description lists the three categories of data returned. It does not mention side effects or costs, but for a zero-argument read-only stats tool this is a minor omission rather than a serious 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 a single, front-loaded sentence with no filler. It states the action first and then names the three output categories efficiently. Every word 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?
With an output schema present, the description doesn't need to detail return values. It covers what the tool does, and with zero parameters and an implied read-only operation, there is little else an agent needs to know to invoke 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?
The tool has no parameters, so the input schema is empty and there is nothing to document. The description appropriately focuses on what the tool returns rather than parameter details. A baseline of 4 is warranted since no parameter semantics 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 the specific verb 'Get' and names a concrete resource bundle: system statistics, knowledge graph counts, and active hardware acceleration tier. This gives an agent a clear idea of what the tool returns and implicitly differentiates it from the sibling memory and graph tools, which are about storage and relationships rather than system metrics.
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?
There is no explicit when-to-use guidance or mention of alternatives. The intended usage is implied by the tool's name and description: call it when you need system or knowledge-graph statistics. However, it doesn't say when not to use it or what other tools might be preferable for similar information.
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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