uctx
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool performs a clearly distinct operation: save adds new context, search and list retrieve it (with different scopes), and forget deletes by id. There is no overlap that would confuse an agent.
Naming Consistency5/5All tool names follow the same verb_noun pattern (save_context, search_context, list_context, forget_context) using snake_case. The naming is completely predictable and consistent.
Tool Count5/5With only four tools, the server is well-scoped for its purpose of managing user context. Each tool is essential and there is no unnecessary bloat or missing core operation for the stated domain.
Completeness4/5The server covers create, read (both search and list), and delete operations. The only minor gap is lack of an explicit update operation, but this can be worked around via delete+save, so it is not a significant dead end.
Average 4.1/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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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?
No annotations are provided, so the description carries the full burden. It discloses that the tool lists items and the sort order ('most recent first'), but it omits the significant behavioral detail that the limit parameter defaults to 50 and may therefore return only a subset rather than all saved items.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the action, scope, and ordering.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and has an output schema, so return-value details are not needed. However, the description omits the default limit behavior and gives no guidance on when to use search_context instead, leaving some contextual gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it does not mention the limit parameter at all. The parameter name and default value in the schema give minimal clues, but the description adds no semantics about how limit interacts with the listing behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' with a clear resource ('user's saved context items') and states ordering ('most recent first'), which unambiguously distinguishes it from the sibling tools save_context, forget_context, and search_context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a user wants to view saved context items, but it does not explicitly state when to prefer this tool over search_context or mention exclusions. It provides clear context for the operation but no explicit alternative 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?
No annotations are provided, so the description carries the full burden. It adds valuable context by explaining that the search retrieves information from other agents, not just the current conversation. It does not explicitly state read-only behavior, but 'search' implies a non-mutating operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the function, and the second sentence adds critical usage guidance. There is no redundancy or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of an output schema, the description is largely complete. It covers purpose and usage, but the unexplained 'limit' parameter is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does 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. It hints that 'query' is a keyword, but it does not explain the 'limit' parameter or its default behavior. This is insufficient for full parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the user's saved context by keyword, using a specific verb and resource. It is distinct from siblings like list_context, forget_context, and save_context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs when to call it: before answering questions about preferences, background, or history, so the answer reflects other-agent context. It does not explicitly name alternatives or exclusions, but the guidance is clear.
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 burden. It clearly indicates a write operation ('Save') and 'durable' suggests persistence, but it does not disclose whether saving the same content again duplicates, overwrites, or merges existing entries, nor any permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: purpose first, then usage guidance, then parameter details with examples. Every sentence contributes useful information; no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a save operation with an output schema, this is nearly complete. It covers purpose, usage, and all parameters. It lacks only minor behavioral details (e.g., duplicate handling), but those are not critical for basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must fully explain each parameter. It does: 'content' is self-contained, 'type' is one of three values, 'tags' are optional keywords, and 'source_app' identifies the saving agent. This adds meaning well beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear purpose: 'Save a durable fact, preference, or note about the user so any agent can recall it later.' This distinguishes it from siblings (forget_context, search_context, list_context) by focusing on durable storage for later retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Use this whenever the user states something worth remembering across sessions and tools' and provides concrete examples. It does not explicitly exclude alternatives, but the context is clear enough for an agent to choose this tool over search or list operations.
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 disclosing behavior. It explicitly says 'Delete', indicating a destructive action, and specifies the scope ('one saved context item'). It also clarifies that the id is a context id, adding helpful context. It could mention irreversibility or permission requirements, but the core destructive behavior is clearly conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence. It front-loads the key action ('Delete') and is concise without any wasted words. Every phrase contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, output schema present), the description covers the essential aspects: what it does, what it operates on, and where the id comes from. It doesn't detail return values (covered by output schema) or error handling, but for a basic delete tool, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines item_id as an integer with no description. The tool description adds meaning by explaining that the id is a saved context item id, and specifically from list_context/search_context. This gives the agent crucial semantic understanding of the parameter that the schema itself lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Delete one saved context item by its id', specifying both the verb (delete) and the resource (saved context item). It also distinguishes itself from siblings by indicating the id origin (from list_context/search_context), making it unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on how to obtain the id ('from list_context/search_context'), implying the workflow of first listing/searching then deleting. It doesn't explicitly state when not to use it or alternatives, but the sibling tool names make the delete-vs-create/retrieve distinction obvious.
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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