Memory-Facade

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose: health check (ping), querying (recall), URL ingestion (ingest_url), session-to-doc conversion (session_to_docs), bank misrouting detection (reroute), and deduplication (dedupe). No two tools could be easily confused.
Naming Consistency4/5
All tools share the consistent prefix 'memory_', but the verb parts vary: single verbs (reroute, ping, recall, dedupe) mix with verb_noun (ingest_url) and prepositional (session_to_docs). The pattern is mostly consistent but has minor deviations.
Tool Count5/5
With 6 tools, the server is well-scoped for a memory management facade. Each tool addresses a distinct operation without overwhelming the agent. The count feels appropriate for the domain.
Completeness3/5
The tool set covers core operations like ingestion, retrieval, reorganization, and deduplication, but lacks explicit create/edit/delete for individual memories, and there is no simple list-all tool. Some gaps exist, but the domain is partially covered.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- Last stable release on August 18, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: URL fetching, auto-routing of banks/tags, article building, auto-linking to related cards, and commit persistence. It also clarifies read-only default and conditional mutation via commit=True. However, with no annotations provided, the description lacks details on error handling, rate limits, or what happens on non-HTML URLs.
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 with two short paragraphs, front-loading the purpose and then detailing the pipeline. Each sentence adds information. The second paragraph could be slightly more terse but is acceptable. The use of backticks for the commit parameter is helpful.
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 the complexity (3 parameters, URL fetching, auto-linking), the description covers the main flow and persistence nuance. However, there is no detail on the output schema (which exists), return values, or error cases. The presence of an output schema means the description needn't explain returns, but the overall completeness for a tool that fetches external URLs could benefit from mentioning failure scenarios.
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 does so by explaining the commit parameter's role (persistence) and hints at explicit_bank in the phrase 'auto-routes bank/tags', but does not elaborate on the exact effect or format of explicit_bank. The baseline is lowered due to coverage, but the description provides partial value beyond the schema.
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 analyzes a URL and produces a compact, cross-linked article card. It distinguishes from siblings by mentioning URL ingestion, which other tools like memory_reroute, memory_recall, or memory_dedupe do not do. The verb 'analyze' and resource 'URL' are specific and action-oriented.
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 for URL analysis and card creation, and notes that it is read-only by default. However, it does not explicitly state when to use this tool versus alternatives (e.g., memory_reroute or memory_recall), nor does it mention prerequisites or when not to use it. The sibling context lacks exclusivity guidance.
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 burden of behavioral disclosure. It clearly states the tool is read-only and never mutates memory, which is a critical safety trait. It also describes the process (merging, deduplication, return format). Missing details about error handling, authentication, or rate limits, but the core safety behavior is transparent enough for a read-only query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (4 sentences) and front-loaded with the main purpose. It uses backticks for code-like terms, is well-structured, and contains no fluff. Every sentence adds value, making it highly efficient for an AI agent to parse.
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 4 parameters and an output schema. The description covers the core operation (query, merge, deduplicate, return citations) and notes the read-only nature. However, it fails to explain the 'query', 'budget', and 'max_items' parameters, and does not detail the output schema structure beyond 'synthesis with citations'. This leaves gaps, but the overall context is minimally adequate.
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 explain all parameters. Only the 'banks' parameter is explained (default ['global-user']). The required 'query' parameter and optional 'budget' and 'max_items' parameters are not mentioned at all, leaving the agent without semantic guidance for those fields. This is a significant gap.
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 'curated semantic recall across Hindsight banks', specifying it queries banks, merges results with provenance, deduplicates, and returns a synthesis with citations. This distinguishes it from sibling tools like memory_ingest_url (ingestion) and memory_dedupe (standalone dedup), making the purpose explicit and differentiated.
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 usage by listing the operation (query, merge, deduplicate, return citations) and explicitly states 'Read-only; never mutates memory', which guides safe invocation. However, it lacks explicit when-to-use versus alternatives (e.g., memory_reroute) and does not provide exclusions or prerequisites, so the guidance is clear but not exhaustive.
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 bears full responsibility for behavioral disclosure. It clearly states that commit=True triggers destructive consolidation, while the default mode is read-only. This gives the agent essential safety context. A minor gap is not describing what happens to the original memory entries post-consolidation.
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 only three sentences, all dense with useful information. No wasted words. The first sentence states the core purpose, the second adds technical detail, and the third provides critical safety caveat. Excellent front-loading.
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, the description does not need to explain return values. There are only 3 parameters with 0% schema coverage, so the description partially compensates for 2 out of 3 parameters. It lacks explanation of sample_limit and what happens after commit=True (e.g., whether the return still shows groups). Overall adequate but not fully comprehensive.
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 explains that bank specifies the memory bank to scan (default 'infra'), and that commit controls whether to perform destructive consolidation. However, sample_limit (default 500) is mentioned only in schema; the description does not clarify its purpose (e.g., limits number of duplicates considered or max memory entries sampled).
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 that the tool finds duplicate/over-fragmented memories and proposes merging. It specifies the input (memory bank), the detection methods (chunk_id collisions and normalized-text duplicates), and the output (proposed groups). This distinguishes it well from siblings like memory_ingest_url or memory_recall.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says that by default the tool is read-only (detect only), and that setting commit=True performs consolidation which is destructive. This provides clear guidance on when to use it safely. However, it could be improved by mentioning when to prefer this over simply recalling memories or other 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, the description bears full burden. It discloses destructive behavior ('relocates (destructive)') and notes read-only default. It mentions deterministic detection. However, it does not cover permissions, error cases, or data safety beyond noting destructiveness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, no fluff. Every sentence provides essential information. Very efficient.
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 3 parameters (0% schema coverage) and no annotations, the description covers destructive behavior and main parameters but misses 'sample_limit'. Output schema exists so return values are not required. Overall functional but incomplete for the unseen parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% so description must compensate. It explains 'source_bank' (bank to scan) and 'commit' (whether to actually move) but entirely omits 'sample_limit' (integer, default 500). Without this, the agent cannot tell what sample_limit controls or how to use it.
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: 'Find memories misrouted to the wrong bank and propose relocating them.' It provides a concrete example ('infra content in global-user') and clearly distinguishes from sibling tools (memory_recall, memory_dedupe, etc.) by focusing on rerouting rather than recall, ingestion, or deduplication.
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 indicates when to use the tool (detect misrouted memories) and contrasts read-only default with destructive commit=True mode. However, it does not explicitly compare to alternatives (e.g., when to use memory_dedupe vs this tool) or state when not to use it.
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 clearly states that the tool groups facts into topic clusters, creates one Article per cluster, cross-links them, and that the default mode is read-only (with `commit=True` persisting the cards). This is a strong disclosure of the tool's core behavior. Missing details like authorization needs or potential side effects, but given the tool appears to be a read-like build operation, the transparency is high.
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 exceptionally concise — three short sentences, front-loaded with the core purpose. The first sentence defines the overall action, the second elaborates on the process, and the third clarifies the commit behavior. No filler, no tautology, every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given moderate complexity (clustering into articles, cross-linking), a simple input schema with 2 params (one boolean default, one array of strings), and no annotations, this description is complete enough. An output schema exists (not provided), so the description doesn't need to explain return values. The process (clustering, article creation, cross-linking) and commit behavior are fully described, leaving no major gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It does so by explaining the `commit` parameter's role in persisting cards vs. read-only default. The `facts` parameter is explained indirectly as 'session's durable facts' that are clustered into Articles. While it doesn't detail the format of each fact string, it provides enough context to understand the purpose of both parameters beyond their names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Turn'), a clear resource ('a session's durable facts'), and identifies the output ('a linked documentation set', 'Article per cluster'). It clearly distinguishes the tool from siblings by describing its unique transformation process — none of the sibling tools (e.g., memory_recall, memory_dedupe) perform this clustering and document creation.
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 the tool is used when you want to turn session facts into documentation, mentioning the clustering and cross-linking behavior. However, it does not explicitly state when to use this tool vs alternatives, nor does it provide conditions under which it should not be used. The explicit mention of 'Read-only by default' with the `commit` parameter provides some guidance on safe usage, but no exclusions or alternatives are named.
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 full burden. It clearly states the function is to report health/connectivity, which is non-destructive, but doesn't elaborate on edge cases or response format (though output schema exists).
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, front-loaded sentence that concisely states the tool's purpose with zero waste.
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 no parameters, no nested objects, and an output schema exists, the description is complete. It fully informs the agent of the tool's purpose with no missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with zero parameters, so the description adds no extra param info but none is needed. Baseline (with high coverage) is 3, but the description's conciseness and clarity earns a higher score.
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 ('Report') and resource ('Hindsight connectivity/health from the facade'), clearly distinguishing it from sibling tools which handle memory operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for connectivity/health checking, which is distinct from siblings. However, it does not explicitly state when to use it versus other tools, though context makes it clear.
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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