UltraPopper

by Pranaav003

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct phase in the workflow (understand, status, clarify, hypothesize, implement, refute, solve, learn), with no overlapping purposes. The descriptions clearly differentiate their roles.
Naming Consistency5/5
All tool names follow the consistent pattern 'popper_<verb>', using snake_case throughout. The naming is predictable and systematic.
Tool Count5/5
With 8 tools covering all major phases of the scientific problem-solving workflow, the count is well-scoped. Each tool earns its place without unnecessary bloat or omission.
Completeness5/5
The tool set provides complete coverage of the intended workflow: from codebase understanding to problem clarification, hypothesis, implementation, refutation, orchestration, and learning. No obvious gaps are present.
Average 3.6/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 134 commits in the last 12 weeks
- No stable releases found
- 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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds the two-call protocol and decision outcomes (survive/refine/exhaust) beyond the schema. However, with no annotations, it fails to disclose side effects, permissions, or error conditions. It is moderately transparent.
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 description is very short (one sentence) and front-loaded, but uses jargon and parenthetical notation that may hinder clarity. It could be more accessible while maintaining 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?
Given the complexity (7 parameters, nested objects, two-call process), the description is insufficient. It does not explain what the returned plan looks like, how to interpret decision outcomes, or what happens on errors. No output schema exists to compensate.
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 100% with detailed parameter descriptions, so the tool-level description adds little extra meaning. The mention of 'verdicts' as the key parameter for the second call is already clear from 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 states the tool returns a firewalled fan-out plan on first call and logs verdicts with decision on second call. It is specific about verb+resource but uses jargon ('firewalled fan-out plan') that may be unclear to an agent. It does not explicitly differentiate from sibling tools.
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 (e.g., popper_understand or popper_hypothesize). The description only outlines the two-call procedure without context on when it's appropriate.
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 must fully disclose behavior. It implies a read-only operation but omits details like whether the .popper/ directory must exist, side effects, or authentication requirements. The term 'knowledgebase summary, phase, and contract stats' is vague.
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 that gets to the point quickly. While concise, it could benefit from slightly more detail without becoming verbose.
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?
Without an output schema, the description should explain what the tool returns. It mentions 'summary, phase, and contract stats' but no format or structure. For a reporting tool, this is insufficiently 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?
The schema describes the only parameter 'problemId' adequately. The tool description adds no new meaning beyond what the schema provides, so a baseline score of 3 is appropriate given 100% schema documentation coverage.
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 reports a status summary, phase, and contract stats. The verb 'Report' and resource references are specific enough to distinguish it from sibling tools like popper_understand or popper_hypothesize.
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. It does not mention prerequisites, typical use cases, or 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'persist', indicating a write operation, but provides no details on side effects, idempotency, error conditions, or what the 'solution record' contains. This is insufficient for behavioral transparency.
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 sentence with no fluff, but it lacks structure (e.g., no separation of purpose from behavior). It is concise but could benefit from additional detail to improve completeness while maintaining 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?
Given there is no output schema and no annotations, the description should cover key aspects like return values, prerequisites (e.g., a surviving hypothesis), and the nature of 'solution record'. It fails to do so, making it incomplete for reliable agent use.
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 input schema has 100% coverage for the single parameter (depth) including its enum and description. The tool description does not add any extra meaning beyond the schema, so it meets the baseline of 3.
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 emits a one-shot implementation prompt and persists a solution record. The verb 'emit' and 'persist' are specific, and 'surviving hypothesis' provides context distinguishing it from siblings like popper_hypothesize or popper_refute.
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 when there is a 'surviving hypothesis', giving some context. However, it lacks explicit guidance on when to use versus alternatives, such as popper_solve or popper_clarify, and does not mention prerequisites or exclusions.
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 alone must disclose behavior. It mentions being 'model-free conductor' and reporting phases, but omits side effects, state changes, prerequisites, or what the output actually contains. Minimal behavioral detail is insufficient for safe agent invocation.
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, well-formed sentence that efficiently conveys the tool's purpose. Every word earns its place, with zero redundancy or irrelevant detail.
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 the tool has 2 parameters, no output schema, and no annotations, the description is too sparse. It does not explain what 'phase' means, the role of 'statement', or the format of the report. An agent lacks sufficient context to use this tool correctly in a workflow.
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?
With 50% schema description coverage, only the 'depth' parameter has a schema description. The tool description adds no semantic clarity: it does not explain 'statement' or how parameters affect behavior. The description fails to compensate for the schema's gaps.
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 reports the current phase and suggests the next popper_* tool to call, positioning it as a model-free conductor. This distinct purpose differentiates it from sibling tools that perform specific actions like hypothesize or refute.
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 should be used for orchestration—deciding which tool to call next—but does not explicitly state when NOT to use it or provide alternatives among siblings. The conductor role is clear, but exclusion criteria are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses two behavioral steps (indexing and persisting) and hints at file creation (into .popper/). However, it does not explain side effects such as whether existing .popper/ is overwritten, whether network access is required, or what happens if called multiple times. The reference to 'Claude's semantic architecture' assumes domain knowledge. More detail on destructive potential and prerequisites would improve transparency.
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 sentence that efficiently conveys the two-step process and parameter role. It is front-loaded with the main action. However, the phrase 'Mechanically index' could be clearer, and the sentence is somewhat dense. Still, it avoids verbosity and is well-structured.
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 tool's complexity (two-step process, nested parameter) and lack of output schema, the description is reasonably complete but has gaps. It does not explain return values or confirmation of success, nor does it mention prerequisites (e.g., must be in a codebase root or have Claude session). Error scenarios are not addressed. The two-call pattern is clearly described, but the overall picture is 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 100%, so the schema already documents both parameters. The tool description adds value by explaining the two-call pattern (interpretation only on second call) and reinforcing the path usage. But it does not significantly enhance meaning beyond the schema descriptions. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's two-step purpose: indexing the codebase into .popper/ and then persisting semantic interpretation on a second call. The verb 'index' and 'persist' specify the action, and the resource (codebase, .popper/ directory) is explicit. This distinguishes it from sibling tools like popper_status or popper_implement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on the two-step calling pattern: omit interpretation on the first scan call and include it on the second. It also explains when to use the path parameter (for subdirectories in multi-project workspaces). However, it does not explicitly state when to use this tool versus alternatives like popper_clarify or popper_hypothesize, though the context of sibling names implies differentiation.
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?
No annotations are present, so the description carries the full burden. It reveals the draft-then-persist pattern but lacks detail on side effects, error handling, or implications of the 'locked contract'. Behavioral traits are partially disclosed.
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 concise sentence that front-loads the key concept. It could be slightly improved by breaking into two sentences for clarity, but it effectively conveys the essence without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, nested objects, no output schema) and the complete schema coverage, the description provides sufficient context for the main workflow. It covers the two-call pattern and parameter usage, though it omits error conditions and return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds context beyond the schema, such as preferring `hypothesesDir` for large payloads and omitting `hypothesis` on the first call. This meaningfully supplements the parameter definitions.
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?
Description uses specific verb 'Draft' and 'persist' with resource 'hypothesis for the locked contract'. It clearly distinguishes from siblings like popper_implement or popper_refute by describing a unique two-call process.
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?
Description explains the two-call pattern (call 1 without `hypothesis`, call 2 with `hypothesis`) and provides guidance on parameter usage ('omit for light mode', 'prefer hypothesesDir for large payloads'). However, it does not explicitly state when not to use the tool or list alternatives.
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?
No annotations are provided. The description discloses the two-call behavior and that the first call returns a drafting prompt and relevant files while the second validates and locks the contract. However, it does not mention potential side effects, prerequisites, or what happens if validation fails, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys the purpose, the two-step process, and the return value in a compact manner. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's role in a larger problem-solving workflow (indicated by sibling tools), the description adequately explains the tool's function and the required two-call sequence. It does not detail return format or error handling, but the core behavior is sufficiently covered for an agent to use the tool 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?
All parameters are described in the schema (100% coverage). The description adds value by explaining the two-call context and that the 'contract' parameter should be omitted on the first call. This clarifies the optionality beyond the schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: converting a raw problem statement into a locked problem contract. It specifies a two-step process (draft then lock) and distinguishes itself from sibling tools like popper_understand and popper_implement by focusing on the initial clarification phase.
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 outlines the two-call usage pattern: first call without a contract, second call with a contract. It does not explicitly state when not to use the tool or mention alternatives, but the context of sibling tools makes its role 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?
No annotations are provided, so the description carries the burden. It discloses that outcomes affect the KB on future refutation, but does not mention idempotency, error states, or safety implications like data persistence.
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?
Two sentences: first defines purpose, second defines kinds and critical requirement. No filler or repetition. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and a single parameter, the description covers the core usage. It explains kinds and evidenceAnchors. Lacks info on whether outcomes are appended or replaced, and the return value, but is generally sufficient for an agent to use 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?
With 100% schema coverage, baseline is 3. The description adds value by explaining the meaning of each kind and emphasizing that held/missed require evidenceAnchors, which the schema lists as optional but the description clarifies as necessary for those cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb and resource: 'Record TDD-execution outcomes for the active contract.' It uniquely distinguishes from siblings like popper_hypothesize and popper_refute by focusing on learning from test results.
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 explains when outcomes are folded in ('on the next popper_refute') and notes that held/missed need evidenceAnchors, providing implicit usage context. However, it does not explicitly contrast with sibling tools.
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:

[![ultrapopper MCP server](https://glama.ai/mcp/servers/Pranaav003/ultrapopper/badges/card.svg)](https://glama.ai/mcp/servers/Pranaav003/ultrapopper)

Score Badge

Copy to your README.md:

[![ultrapopper MCP server](https://glama.ai/mcp/servers/Pranaav003/ultrapopper/badges/score.svg)](https://glama.ai/mcp/servers/Pranaav003/ultrapopper)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/Pranaav003/ultrapopper'

If you have feedback or need assistance with the MCP directory API, please join our Discord server