beads-viz
Server Quality Checklist
Latest release: v0.1.2
- Disambiguation4/5
visualize-tasks is clearly the entry point for opening the DAG, while poll-tasks returns current task data and show-task targets one task's details. Poll-tasks and show-task could be confused at a glance, but the singular-vs-plural and detail-vs-refresh descriptions separate them.
Naming Consistency4/5All tool names follow a verb-noun pattern with hyphen separators (visualize-tasks, poll-tasks, show-task). The only inconsistency is plural 'tasks' in two names versus singular 'task' in show-task.
Tool Count4/5Three tools is a reasonable, focused set for a visualization server: one user-facing visualization tool plus two support tools for data refresh and detail lookup. It is not bloated, though two app-only tools reduce the agent-facing surface.
Completeness3/5The set covers the main visualization workflow (open DAG, refresh data, view task detail), but two tools are marked app-only and there is no explicit task-list/search tool or way to act on tasks beyond viewing, which may leave agents without a clear path from visualization to action.
Average 3.7/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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.
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.jsonto 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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral disclosure burden. 'Get' strongly implies a read-only, non-mutating operation, and 'app-only' hints at access restrictions. Still, it does not clarify authentication requirements, error behavior, or what 'detailed information' includes, which is meaningful given there is no output schema.
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 filler. Every word contributes to either the operation, the target resource, or the usage context, and it is easy to scan.
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?
The tool is simple: one required parameter, fully described in the schema, and the description gives the purpose and app context. The lack of an output schema makes the return format ambiguous, but for a basic singular-task lookup the definition is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents taskId and its example format. The description adds little beyond 'specific task', which weakly maps to the taskId parameter but provides no additional meaning or usage details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('detailed information about a specific task'), making the tool's purpose unambiguous. It does not explicitly contrast itself with the sibling tools, but 'specific task' signals a singular lookup versus broader task visualization/polling.
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 phrase 'app-only, used by the visualization UI' provides contextual guidance about intended usage and implies it is not for general-purpose agent calls. However, it does not explicitly describe when to prefer show-task over poll-tasks or visualize-tasks, leaving some routing decision to inference.
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 must disclose side effects and runtime behavior. It says 'Opens a visual app,' which implies a long-running or interactive process, but does not state whether it is read-only, blocking, requires a display, or how it terminates. The agent cannot anticipate side effects or resource implications.
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?
Two sentences with no filler; the main action and scope are front-loaded and the second sentence adds concrete detail on what the visualization shows. Every word earns its place.
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?
For a zero-parameter visualization tool, the description covers what it shows and how it is presented, but it omits usage conditions relative to sibling tools and behavioral details like whether the app runs asynchronously or requires user interaction. With no output schema or annotations, these gaps leave an agent without enough context to fully anticipate the tool's runtime profile.
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 tool has zero parameters, so schema coverage is trivially complete and there is nothing for the description to add about parameter meaning. The baseline of 4 applies.
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?
Description states a specific verb ('Visualize'), a specific resource ('Beads task graph'), and the output form ('interactive DAG'), and names the content (dependencies, status, progress). This clearly distinguishes it from sibling tools like poll-tasks and show-task, which are about individual task polling/detail.
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 when to use it (when you need a graph-level view of task dependencies and progress), but it never explicitly contrasts with sibling tools such as poll-tasks or show-task, nor states when those alternatives would be more appropriate. No exclusions or conditions are provided.
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 context. 'Poll' and 'refresh' imply a non-mutating read of current task data, and 'app-only' is a useful access constraint. However, it does not disclose output format, polling mechanics, potential side effects, or whether it returns a snapshot vs. a change set, leaving some ambiguity.
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 sentence with no wasted words. The main action is front-loaded, and the parenthetical adds the necessary contextual constraints without bloat.
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 zero-parameter app-only polling endpoint, this is largely complete: it states what data is returned, who should use it, and why. It does not explicitly describe the response shape or any refresh semantics, but the low complexity and absent output schema make that a minor gap rather than a blocking one.
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 input schema is empty with 100% coverage and zero parameters, so the description has no parameter behavior to document. Per baseline for zero-parameter tools, a 4 is appropriate; no parameter information is missing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Poll') and resource ('current task data'), and clarifies that it is app-only and exists for visualization UI refresh. This distinguishes it from the sibling tools in purpose, though it does not explicitly name them or contrast directly.
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 phrase 'app-only, used by the visualization UI for refresh' gives a clear usage context and implicitly excludes general-purpose task querying or one-off viewing. It stops short of explicitly naming when-not-to-use alternatives, but the context is strong enough for an agent to route correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/pyros-projects/beads-viz'
If you have feedback or need assistance with the MCP directory API, please join our Discord server