pdml-agent

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 operation: listing runs, retrieving config, getting results, comparing runs, searching logic definitions, and running experiments. Descriptions clearly differentiate purposes and provide usage context, leaving no ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with underscores (get_experiment_config, list_experiments, get_results, compare_runs, search_logic_definitions, run_experiment). No mixing of styles or irregular verbs.
Tool Count5/5
With 6 tools covering the core experiment lifecycle (listing, inspecting, comparing, searching, running), the count feels well-scoped and purposeful. Not excessive or sparse for this domain.
Completeness4/5
The tool set covers the primary workflow: discover runs, inspect configuration and results, compare, search logic, and execute new runs. A minor gap is the lack of a tool to cancel or update running experiments, but for the stated purpose of planning and analyzing, this is acceptable.
Average 4.2/5 across 5 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral disclosure. It reveals key behaviors: the default epoch is the final one, the naming convention for constraint security and predictive performance metrics, and crucially that missing metrics are returned as null instead of the -1 sentinel used by the training script. This eliminates ambiguity and prevents misinterpretation of sentinel values.
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 composed of three sentences that are concise and front-loaded with the primary purpose. It wastes no words, though it could be slightly more structured by separating the parameter default from the metric naming conventions. Overall, it is efficient and well-organized.
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 has 2 parameters, no output schema, and no annotations, the description covers the default behavior, metric naming, and null handling. However, it fails to describe the return format (e.g., dictionary or list), possible error conditions (e.g., invalid experiment_id), or the full set of metrics. It is adequate but leaves gaps for an agent to infer.
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 default behavior of the epoch parameter (defaulting to final epoch) and implicitly linking experiment_id to the run. However, it does not explicitly describe the parameters' types, constraints, or the experiment_id parameter's purpose beyond the tool context. The added value is moderate but not fully compensatory.
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 retrieves metrics for a specific epoch of a run, defaulting to the final epoch. This is a specific verb-resource combination that distinguishes it from siblings like list_experiments (listing all experiments) or get_experiment_config (getting configuration). The mention of defaulting to the final epoch adds precision.
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 explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, nor does it reference sibling tools like compare_runs or search_logic_definitions. The usage context is only implied by the tool's purpose (getting metrics), but no exclusions or prerequisites are 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?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It tells the user about search scope and return type (operators and location) but does not disclose whether the operation is read-only, its performance characteristics, or any side effects like network calls. For a search tool, this is adequate but not rich in detail.
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 three sentences and efficiently front-loads the core purpose in the first sentence, followed by scope details and usage guidance. Each sentence contributes distinct information without redundancy, though a slightly more condensed phrasing could improve it further.
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 has a single parameter with no enums, no output schema, and no nested objects, the description adequately covers the search behavior, parameter semantics, and usage context. It explains what fields are searched and how the empty query works, which is sufficient for an agent to use the tool effectively in the context of understanding logic implementations.
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 has 1 parameter (query) with 0% description coverage and no schema-level descriptions, so the description must compensate. The description explains the query parameter well: it matches on class name, docstring, and filename, and states that an empty query returns all entries. This adds meaningful semantics beyond the schema's default field.
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 searches for differentiable logic implementations, specifying the search scope (class name, docstring, filename) and what it returns (operators and location). This is specific and distinguishable from siblings like get_results or run_experiment, though it does not explicitly name a sibling for differentiation.
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 context for when to use the tool, explicitly stating 'Use this to understand what a logic does before interpreting a result or proposing a run that uses it.' This gives actionable guidance on the tool's role in the workflow, though it does not mention when not to use it or list alternatives explicitly.
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 discloses that the tool returns only differing config keys and deltas on predictive performance and constraint security, which is informative. However, it does not explicitly state that the tool is read-only (no destructive side effects), which would be a minor improvement for 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 three sentences, each serving a distinct purpose: stating the action, detailing the output, and providing usage guidance. No extraneous words; every sentence earns its place.
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 low complexity (two string parameters, no output schema, no annotations), the description covers the core functionality and output format. It mentions what is returned (differing config keys, deltas on metrics). However, it could be more complete by describing the return structure (e.g., a JSON object with keys for config and metrics). The lack of output schema makes this gap more noticeable, but the description is still sufficient for an agent.
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%, meaning the input schema provides no property descriptions. The description does not mention the parameter names 'experiment_id_a' and 'experiment_id_b' or explain their format, source, or constraints. While the names are self-explanatory, the description should add detail (e.g., 'unique identifiers for runs') to 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 clearly states the verb 'diff' and the resource 'two runs', specifying that it compares configuration and headline metrics. It distinguishes itself from the sibling tool 'get_results' by mentioning it returns only differing config keys and deltas, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises to prefer this tool over calling 'get_results' twice when the question is about what changed between two runs. This provides a clear usage guideline and directly contrasts with an alternative, helping the agent decide when to invoke this tool.
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 behavioral burden. It describes the return structure (fields: experiment_id, constraint, dataset, seed, logic, epochs) and explains constraint filter behavior (accepts class name or folder name). This adds meaningful transparency beyond just 'list runs'.
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 three concise sentences, each serving a purpose: stating the action, detailing the output, and providing usage guidance. No redundancy or unnecessary words.
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's simplicity and the presence of an output schema, the description covers all essential aspects: what is returned, the filter format, and the inter-tool dependency. It is complete for effective 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?
Schema description coverage is 0%, so the description must compensate. It provides specific detail for the constraint parameter (accepts class name or folder name) but does not describe the logic or dataset parameters beyond being optional filters. Some added value, but incomplete.
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 specifies the verb 'List', the resource 'completed experiment runs', and notes optional filters. It differentiates from siblings by stating that this tool returns experiment_ids used by other experiment tools, making its role distinct.
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 instructs to call this tool first because every other experiment tool takes an experiment_id from here. This provides clear context for usage, though it does not mention when not to use it or name specific 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?
No annotations are provided, so the description carries the full burden. It discloses that the tool reads the command line recorded in the run's own output, and emphasizes that it recovers 'what actually ran rather than what was intended'. This is valuable behavioral context for a read-only introspection tool, though it does not mention auth requirements or rate limits.
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 three concise sentences, each adding value. The first sentence states the purpose, the second explains the mechanism, and the third provides usage guidance. No wasted 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 has no output schema, the description appropriately skips explaining return values. It covers what the tool does, how it works, and when to use it. However, it does not mention the format of the returned configuration (e.g., as a JSON object or command-line string), which would be helpful for the agent to know how to use the output.
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 0% (the parameter names are not described in the schema), so the description must compensate. The description does not directly describe the 'experiment_id' parameter, but the context of 'recover the exact configuration a run was trained with' strongly implies that the parameter identifies the run. With only one required parameter and no enums, this is sufficiently clear.
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 verb 'recover' and the resource 'exact configuration a run was trained with', distinguishing it from siblings like 'list_experiments' (which lists experiments) and 'run_experiment' (which creates a new run). It explains that the configuration is read from the run's own output, adding specificity.
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 'Use it before proposing a new run based on an existing one', providing clear guidance on when to use this tool. However, it does not explicitly state when not to use it or mention alternatives among the siblings, though the context implies it's for inspection before modification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: dry_run validates without side effects, execution requires operator approval and server enablement, possible statuses are named, and training time is estimated ('minutes even for one epoch'). Also mentions oracle_steps/restarts control adversarial attack cost.
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 well-structured and front-loaded with the key distinction. Each sentence provides essential operational guidance—no filler or repetition. The three paragraphs flow logically from purpose to validation to execution.
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?
For a complex compute-consuming tool with no output schema, the description covers safety gates, refusal handling, time cost, and next steps (get_results). It falls short only in not explaining parameter meanings, but overall it is remarkably complete for an agent to use safely.
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 explains dry_run and oracle_steps/oracle_restarts, but 10 of 13 parameters (dataset, constraint, logic, epochs, batch_size, lr, seed, delay, epsilon, results_dir) have no semantic explanation beyond their names. This is a notable gap for critical 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 clearly states it 'Plan a training run, or execute one. The only tool that consumes compute.' This specifies the verb (plan/execute), resource (training run), and distinguishes from sibling read-only tools like get_results and list_experiments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit workflow guidance: 'Do that first and show the plan' (dry-run), 'Do not retry a refused or declined call; report it', and 'A completed run returns the experiment_id to pass to get_results'. It also notes it is the only compute-consuming tool, indicating when this tool is appropriate.
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:

[![pdml-agent MCP server](https://glama.ai/mcp/servers/HappyHackingOrange/pdml-agent/badges/card.svg)](https://glama.ai/mcp/servers/HappyHackingOrange/pdml-agent)

Score Badge

Copy to your README.md:

[![pdml-agent MCP server](https://glama.ai/mcp/servers/HappyHackingOrange/pdml-agent/badges/score.svg)](https://glama.ai/mcp/servers/HappyHackingOrange/pdml-agent)

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/HappyHackingOrange/pdml-agent'

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