@arizeai/phoenix-mcp

Official

by Arize-ai

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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

Latest release: v1.0.16
Disambiguation2/5
Many tools overlap in functionality, such as multiple ways to retrieve prompts (get-prompt, get-prompt-by-identifier, get-latest-prompt) and experiments (get-dataset-experiments, list-experiments-for-dataset). This would likely cause an agent to select the wrong tool.
Naming Consistency4/5
Tool names consistently use hyphens and a verb-noun pattern (e.g., add-dataset-examples, get-dataset, list-datasets). While there are minor deviations like 'upsert-prompt' and 'phoenix-support', the overall pattern is predictable.
Tool Count3/5
27 tools is on the higher end but still manageable for a broad domain like observability. However, the redundancy inflates the count unnecessarily, making it feel heavier than needed.
Completeness3/5
The server covers many areas (datasets, experiments, prompts, traces, spans, sessions, annotations) but lacks essential operations like delete or update tools for resources. This leaves notable gaps for common workflows.
Average 3.3/5 across 27 of 27 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- 138 of 950 issues responded to in the last 6 months
- No commit activity data available
- Last stable release on June 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'Create or update' but only describes creation, creating ambiguity about the update case. No side effects, idempotency, or permission requirements are disclosed. With no annotations, the agent lacks critical behavioral context for a mutation tool.
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 relatively short but includes a redundant statement ('Create or update' vs 'Creates a new prompt') and an example that doesn't illustrate optional parameters. It could be more streamlined and informative.
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?
With 6 parameters, no output schema, and no annotations, the description fails to provide enough context for an agent to invoke correctly. Missing details include update semantics, parameter constraints, and return type beyond a vague 'confirmation message'.
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%, yet the description only mentions 'template and configuration' and 'model settings' without detailing individual parameters. The example omits optional fields like model_provider, model_name, and temperature, so the agent gains little insight 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 states 'Create or update a prompt with its template and configuration', clearly indicating the action and resource. It distinguishes from sibling tools like get-prompt and list-prompts which are read-only. However, it does not clarify the update behavior, focusing only on creation.
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 explicit guidance is given on when to use this tool versus alternatives like get-prompt or add-prompt-version-tag. The description does not mention prerequisites or when an update is appropriate, leaving the agent to infer context.
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 exist, so the description carries the full burden. It mentions it lists experiments and returns an array, but lacks disclosure of side effects, authentication, rate limits, or data scope.
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 concise with an example and expected return, but lacks structure (e.g., no parameter descriptions). It is not 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?
Given no output schema, the description vaguely mentions 'array of experiment objects with metadata', which is insufficient. It also does not address the sibling overlap.
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%, and the description provides no explanation of parameters (dataset_id, dataset_name, limit). The example uses a base64 ID but does not clarify semantics.
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 it lists experiments for a dataset, but does not differentiate from the sibling tool 'list-experiments-for-dataset', leading to potential confusion.
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 vs alternatives (e.g., list-experiments-for-dataset), nor any prerequisites or exclusions. Only a generic example is provided.
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 carries full burden but only states the return type ('Array of session objects ordered by the requested sort order'). It lacks details on side effects, permissions, error handling, or empty result behavior. The behavioral disclosure is minimal.
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 plus an example, front-loading the core purpose. Every sentence adds value without redundancy. The structure is ideal for quick comprehension.
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 3 parameters with no schema descriptions, no output schema, and no annotations, the description should compensate but does not. It omits parameter details, output structure (e.g., fields of session objects), and sorting semantics. The example partially fills gaps but overall is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, yet the description adds no explanation for any parameter. The example implies usage of project_identifier and limit, but does not formally describe their meaning, constraints, or interaction (e.g., ordering). No parameter semantics provided.
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 action ('List sessions for a project') and explains that sessions are 'conversation flows grouped across traces,' distinguishing from sibling tools like get-session and list-traces. It provides a concrete example to reinforce the purpose.
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 is given on when to use this tool instead of alternatives, such as get-session for a single session or list-traces for trace-level data. The description does not mention prerequisites, filters, 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.
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 explains the return structure with a detailed example (template, model config, invocation parameters). However, it does not state any potential errors, authentication requirements, or what happens if no versions exist. The read-only nature is implied but not explicit.
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 front-loaded with purpose, followed by a useful example. However, the example is verbose and could be shortened while retaining clarity. Some sentences could be trimmed without losing meaning.
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 lack of output schema and low schema coverage, the description should provide more completeness. It fails to describe the parameter semantics fully, and does not clarify how 'latest' is determined (e.g., by creation date). Missing details about ordering or error cases.
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 coverage is 0%, so the description must compensate. The only parameter, 'prompt_identifier', is not described explicitly. The example uses a prompt name, suggesting it is an identifier, but no clarification on format (name vs ID) or source. Partial compensation but inadequate.
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 action ('get the latest version') and resource ('prompt'). The name 'get-latest-prompt' and example usage with a prompt name make the purpose unambiguous. However, it does not explicitly distinguish from sibling tools like get-prompt-version, which retrieve specific versions; the distinction is implied by the word 'latest'.
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 alternatives. It does not mention that this is for retrieving the most recent version, while get-prompt-version or get-prompt-version-by-tag are for specific versions. No 'when to use' or 'when not to use' information.
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 only states it is a read operation and expected return, but omits behavioral traits like error handling, authentication needs, or side effects. Minimal disclosure beyond the obvious.
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?
Extremely concise and well-structured. Front-loaded with key purpose, followed by a clear example and expected return. Every sentence adds value without redundancy.
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 no output schema, the description mentions 'project object with metadata' but is vague on specific fields. It covers the basic retrieval use case but lacks details on error conditions or full structure. Adequate for a simple getter but not comprehensive.
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%. The parameter 'project_identifier' is described only as 'name or ID' via the example. No constraints, format, or enumeration are provided. The description adds little beyond what the parameter name implies.
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 it gets a project by name or ID. The verb 'Get' and resource 'project' are specific. It distinguishes from sibling tools since they target different resources (datasets, prompts, etc.), but does not explicitly differentiate within the same resource class.
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., list-projects, get-prompt). No prerequisites or exclusions are mentioned. The example shows a simple usage but lacks context for appropriate selection.
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 carry the burden of behavioral disclosure. It explains the concept of experiments but does not mention read-only nature, authentication needs, rate limits, or any side effects. The description provides minimal behavioral context.
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 reasonably well-structured, including a brief conceptual note and an example usage with expected return. It is not overly verbose, though some of the explanatory context (e.g., 'Experiments are collections...') could be shortened without losing value.
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 lack of annotations, output schema, and incomplete parameter explanations, the description is insufficient. It omits details on how to use the 'dataset_name' parameter, the effect of 'limit', pagination, ordering, and any error conditions, leaving significant gaps for an AI 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?
The schema has 3 parameters with 0% description coverage. The description does not explain the 'dataset_name' or 'limit' parameters, only indirectly referencing 'dataset_id' via the example. It fails to add meaning beyond the schema's type and constraints.
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 'Get a list of all the experiments run on a given dataset,' specifying the verb and resource. It includes an example usage, but does not differentiate from the very similar sibling tool 'get-dataset-experiments', which reduces clarity.
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 provides an example usage with a dataset ID, implying how to use the tool, but lacks explicit guidance on when to prefer this tool over alternatives like 'get-dataset-experiments' or 'get-experiment-by-id'. No when-not or exclusion criteria are given.
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 must carry the burden. It explains that projects are containers for observability data and gives an example return format. However, it does not disclose behaviors like pagination, rate limits, or whether the list is exhaustive over time.
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 relatively compact but includes an example and explanation of projects, which may be slightly verbose. The structure is clear with a header, explanation, example, and expected return, but could be trimmed.
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 simplicity and the lack of output schema, the description provides an example return object but omits parameter guidance. It is minimally complete for a straightforward list tool but could be more thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has three parameters (limit, cursor, include_experiment_projects) with 0% coverage in the description. The description does not mention any parameters or their meaning, so it adds no 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 'Get a list of all projects' and explains what projects are, making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'get-project' or other list tools, which would merit a higher score.
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 an example usage ('Show me all available projects') but no guidance on when to use this tool versus alternatives like 'get-project' for a single project, or when not to use it. No context about prerequisites or limitations.
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 behavior. It states it returns a list of prompt objects but does not clarify read-only nature, authentication needs, or any side effects. The limit parameter's effect is not explained.
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 relatively concise with a clear structure: purpose, definition, expected return, and example. The definition of prompts may be slightly redundant but adds context without excessive length.
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 no output schema and one optional parameter, the description fails to mention pagination or that the limit constrains results. The phrase 'list of all the prompts' is misleading when limit defaults to 100. No return type details beyond example.
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%, yet the description does not mention the 'limit' parameter. The agent must infer its meaning from the schema alone, which is insufficient for complete understanding.
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 'Get a list of all the prompts' and defines what a prompt is. It distinguishes the tool from others by specifying it returns a list of prompts, not versions or individual prompts.
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 siblings like get-prompt or list-prompt-versions. The description does not provide context for selecting this tool over alternatives.
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 mentions 'pagination support' but does not explain the pagination mechanism (e.g., cursor or offset). It also omits error handling (e.g., prompt not found) and ordering details. The expected return is described vaguely as 'Array of prompt version objects with IDs and configuration'.
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 concise with three sentences plus an example. It is front-loaded with the primary action. However, it could be more structured by separating usage from return format.
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 insufficient. It lacks details on pagination behavior, error conditions, and parameter formats, leaving significant gaps for the agent to understand correct invocation.
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 only references 'prompt identifier' via example, not its format (name or ID). The 'limit' parameter is mentioned only in schema, and the description adds no explanation of how pagination works.
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 'Get a list of all versions for a specific prompt' which identifies the action (get list) and the resource (prompt versions). It distinguishes from siblings like 'get-prompt-version' (singular) and 'list-prompts' (prompts, not versions).
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 an example usage but lacks explicit guidance on when to use this tool versus alternatives (e.g., get-prompt-version). No 'when-not-to' or conditions for use are given.
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?
The description discloses the return structure (dataset ID, version ID, array of examples) and provides an example. However, it does not mention auth requirements, rate limits, or potential side effects. Since annotations are absent, the description partially fulfills the burden.
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 concise with three sentences covering purpose, example, and return format. The example with a specific ID adds some overhead but does not detract significantly from clarity.
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 4 parameters with no schema descriptions and no output schema, the description should explain parameters and possibly differentiate from siblings. It partially describes the return but leaves parameter semantics unaddressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The tool description only mentions a specific dataset ID in the example but does not explain the meaning of any of the four parameters (dataset_id, dataset_name, version_id, splits). This provides no useful semantics beyond the schema itself.
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 'Get examples from a dataset' and elaborates on what examples are (input/output/metadata). It includes an example usage and expected return format, making the tool's purpose specific and distinct from siblings like 'add-dataset-examples'.
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 does not explicitly provide when-to-use or when-not-to-use guidance, nor does it mention alternative tools. Usage is only implied by the example, leaving an agent without clear context for selection.
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 full burden. It includes an expected return format with an example, which adds transparency. However, it does not disclose behavioral traits such as potential side effects, authorization requirements, rate limits, or constraints on filtering. The example provides some context but not comprehensive behavioral depth.
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 relatively long: it includes a conceptual explanation, example usage, and a full JSON example. The main action is front-loaded. However, some content (e.g., the full example object) could be considered excessive or redundant. It is adequate but not optimally concise.
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's complexity (11 parameters, no output schema, no annotations), the description should be highly complete. While it explains what spans are and shows a return example, it fails to detail the purpose of many filtering parameters or pagination (cursor, limit). It also lacks any mention of required parameters or default behaviors beyond limit. The description is incomplete for an agent to use the tool effectively.
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?
The input schema has 11 parameters with 0% description coverage (no parameter-level descriptions). The tool description mentions 'filtering criteria' and gives general examples but does not explain individual parameters like start_time, end_time, trace_ids, etc. This is insufficient compensation for the lack of schema descriptions, leaving much ambiguity for the agent.
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 action ('Get spans from a project with filtering criteria') and explains what spans are. It includes example usage that reinforces the purpose. However, it does not explicitly distinguish itself from sibling tools like get-trace or list-traces, which reduces sibling differentiation.
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 provides example usage scenarios (recent spans, time range) that imply when to use the tool. However, it does not specify when not to use it or mention alternatives (e.g., get-trace for a single trace, list-traces for summary). The guidance is present but not explicit about exclusions or comparative context.
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 bears full burden. It states grouping and ordering behavior, and mentions expected return format. However, it does not disclose potential side effects, permissions, pagination, 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 efficient: three sentences plus example usage and expected return. No fluff, every sentence adds value.
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 no output schema, the description provides a high-level return format. However, it lacks parameter explanations and does not cover edge cases or constraints for a tool with 5 parameters.
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% for 5 parameters. The description only hints at 'limit' via examples but does not explain project_identifier, since, last_n_minutes, or include_annotations beyond their schema definitions.
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 verb 'List' and resource 'traces for a project', including that it groups spans and returns newest first. It does not explicitly differentiate from sibling tools like get-trace, but the purpose is specific enough.
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 is provided on when to use this tool versus alternatives (e.g., get-trace for a single trace, get-spans for raw spans). The description lacks explicit usage context 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 must carry full burden. It mentions return status (204) but contradicts itself by also stating 'confirmation message'. It does not disclose side effects like overwriting existing tags or error conditions.
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 short and front-loaded with the purpose, including an example and expected return. However, the repetition of 'Add a tag' and the slight inconsistency reduce efficiency.
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 no output schema, the description fails to fully explain return values (contradicts 204 no content with confirmation message). It omits error scenarios and does not cover the optional parameter. The tool is not fully contextualized.
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 coverage is 0%, and the description adds minimal meaning beyond the schema. The example clarifies 'name' but does not explain the optional 'description' parameter. The parameters are not fully described.
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 adds a tag to a specific prompt version, which is a specific action distinguishing it from siblings like list-prompt-version-tags and get-prompt-version-by-tag.
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 provides an example but lacks explicit guidance on when to use this tool versus alternatives, such as listing or retrieving tags. Usage is implied but not clearly differentiated.
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 full burden. It states a safe read operation ('Get a single session'), which is consistent with the tool's behavior. However, it lacks details on potential errors, permissions, or edge cases. The expected return is mentioned but not the structure of the session object.
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 concise with 4 sentences plus an example. It is front-loaded with the purpose. The example is useful but could be more precise. No unnecessary information.
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?
No output schema is provided, so the description should elaborate on the return structure. It mentions a 'session object and, optionally, its annotations', but without further detail. Given the tool's simplicity, this is minimally adequate but not 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 coverage is 0%, so the description must explain parameters. It does mention that session_identifier can be a GlobalID or user-provided session_id, and that include_annotations is optional. However, it does not specify the format of the identifiers or what annotations include, leaving room for ambiguity.
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 it retrieves a single session by an identifier, distinguishing it from list-sessions which returns multiple sessions. The verb 'get' and phrase 'single session' are specific. However, it does not explicitly differentiate between using GlobalID vs user-provided session_id in terms of syntax, which could be clearer.
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 explicit guidance on when to use this tool versus alternatives like list-sessions. The example 'Show me session "chat-123"' implies usage when an identifier is known, but there is no discussion of prerequisites or scenarios where other tools are more appropriate.
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 full burden. It states that the tool returns a version with its template and configuration, implying a read-only operation. However, it does not disclose potential error cases (e.g., missing tag), rate limits, or authentication needs.
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 very concise, containing three efficient sentences: a definition, an example, and the expected return. No unnecessary words, and the format is easy to parse quickly.
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 only two parameters and no output schema or nested objects, reducing complexity. The description covers the core functionality and return shape, but lacks detail on parameter definitions (especially prompt_identifier). For a simple tool, this is adequate but not fully complete.
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 coverage is 0%, and the description adds minimal meaning. The term 'tag name' is clarified, but 'prompt_identifier' is ambiguous (could be ID or name). The example shows using a prompt name, but the schema does not specify format or allowed values. The description should define what prompt_identifier is and how tag_name is used.
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?
Description clearly states the tool retrieves a prompt version by tag and lists returned components (template, model config, invocation params). Example is helpful. However, it does not explicitly distinguish from sibling tools like 'get-prompt-version' which uses a different identifier.
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?
Provides an example usage ('get the production tagged version') which implies when to use. Lacks explicit guidance on when not to use, alternatives, or prerequisites. With many sibling tools (e.g., list-prompt-version-tags, add-prompt-version-tag), more direction would help.
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 the full burden. It only states that the tool lists configs and returns an array, but it does not disclose any behavioral traits such as whether the operation is read-only, any authentication requirements, or rate limits. The description adds minimal behavioral context beyond what is obvious.
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 extremely concise with only two sentences plus an example and expected return. Every sentence provides necessary information without redundancy. It is front-loaded with the core purpose.
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?
For a simple tool with one optional parameter and no output schema, the description is mostly adequate. It explains the purpose and expected return, but it omits any explanation of the 'limit' parameter, which could lead to misuse. The missing parameter description is a notable gap.
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?
The schema has 0% description coverage for the only parameter 'limit', and the tool description fails to mention this parameter at all. The description does not add meaning beyond the schema, which already defines default and constraints. Given the low coverage, the description should compensate but does not.
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 'List Phoenix annotation configs' and explains what annotation configs are, making the tool's purpose unambiguous and specific. It distinguishes from sibling list tools by focusing on annotation configs as opposed to datasets or prompts.
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 provides an example usage ('Show me all annotation configs') which implies when to use this tool, but it does not offer explicit guidance on when not to use it or compare it to alternative siblings. The context is implied but lacks clarity for the AI agent to make an informed decision.
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 full burden. It states the expected output (template, model config, invocation params) but does not disclose potential errors (e.g., missing identifier), authentication needs, or side effects. As a simple read operation, the description is adequate but not comprehensive.
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: purpose, example usage, expected return. It is concise, front-loaded, and every sentence adds value. 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 single parameter and no output schema, the description covers input and output well. However, it does not differentiate from the similar sibling 'get-latest-prompt', missing an opportunity to clarify the tool's unique role.
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 provides no description for the parameter 'prompt_identifier' (0% coverage). The description adds crucial semantics: it accepts a name or ID, and includes an example ('article-summarizer'). This meaningfully enhances the bare 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 verb 'Get' and the resource 'a prompt's latest version by its identifier (name or ID)', and specifies the return contents. However, the sibling tool 'get-latest-prompt' exists with similar purpose, so some ambiguity remains despite the description.
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 explicit guidance is provided on when to use this tool versus alternatives like 'get-prompt-version' or 'get-prompt-version-by-tag'. The description does not mention when not to use or suggest alternative tools.
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 full burden. It explains the tool returns tag objects with names and IDs and mentions pagination support. However, it does not describe how pagination works (e.g., whether there is a cursor or offset), nor does it discuss any authentication or side-effect constraints. The read-only nature is inferred but not explicit.
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 long, starting with a clear purpose, followed by an example, and then the expected return format. It is efficient and front-loaded with the essential information. Minor improvement could be to separate the example into a separate line, but overall it is well-structured and concise.
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?
For a tool with 2 parameters and no output schema, the description covers the core behavior: listing tags for a given version with pagination and returns tag objects. However, it lacks details on how to paginate through results (e.g., use limit parameter and no offset?) and does not specify all fields of tag objects beyond 'names and IDs.' The completeness is adequate but not thorough.
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?
The input schema has 0% description coverage, meaning no parameter descriptions in the schema itself. The tool description mentions pagination support, which implicitly relates to the limit parameter, and shows an example prompt_version_id. However, it does not explain the default limit, the range, or the meaning of the return array. The added value is minimal beyond what the parameter names imply.
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 "Get a list of all tags for a specific prompt version." This is a specific verb (get) and resource (tags for a prompt version). The sibling tools include add-prompt-version-tag and get-prompt-version-by-tag, which are distinct actions, so the purpose is well-differentiated.
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 provides an example usage but no explicit guidance on when to use this tool versus alternatives. It does not mention that for adding tags you should use add-prompt-version-tag, or for retrieving a version by tag use get-prompt-version-by-tag. The context is implied but not directive.
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 partially discloses behavioral traits, such as automatically adding metadata indicating synthetic generation via MCP, and returning a confirmation. However, it does not mention side effects like appending vs. replacing, or limits on example count.
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 well-structured in three paragraphs, front-loads the purpose, and includes usage guidance and an example. It is concise but could be slightly trimmed without losing information.
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 low schema coverage, the description covers the operation, example structure, automatic metadata, and expected return. It references a sibling tool for pre-checks. Missing details like error handling or dataset existence validation, but sufficiently complete for its complexity.
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 mentions that examples contain input, output, and metadata but does not clarify the structure beyond the schema names. It lacks details on formats, constraints, or optional fields, adding minimal value over the schema.
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 adds examples to an existing dataset, specifying each example includes input, output, and metadata. It also references a sibling tool (get-dataset-examples) for checking existing examples, distinguishing the tool's purpose.
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 instructs to use get-dataset-examples to check existing examples and avoid duplicates, and to follow existing patterns. This provides clear guidance on when and how to use the tool, though it does not explicitly 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It mentions that the return includes 'metadata and version information', which hints at read-only behavior. However, it does not explicitly state that it is read-only, nor does it document side effects, authentication requirements, or error handling. For a simple retrieval, this is marginal but not fully transparent.
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 extremely concise: one sentence for purpose, one for example, and one for expected return. It front-loads the main action and uses no filler words. Every sentence adds value.
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 simplicity (2 params, no output schema), the description covers the core functionality and return type. It lacks details on behavior when dataset is not found, both parameters provided, or authorization. However, for a straightforward retrieval, it is largely complete. A slightly richer description would merit a 5.
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 0% description coverage for the two parameters (dataset_id, dataset_name). The description partially compensates by stating 'by name or ID' and providing an example using name. This clarifies the purpose of each parameter but does not specify if they are exclusive, optional, or have format constraints. The meaning is improved over the bare schema but still ambiguous.
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 'Get dataset metadata by name or ID', specifying the verb (get) and resource (dataset metadata). It distinguishes from sibling tools like 'add-dataset-examples' and 'get-dataset-examples' which handle examples, and 'list-datasets' which lists all datasets, by focusing on retrieval by specific identifier.
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 provides an example usage ('Show me the dataset "my-dataset"') which implies typical use. However, it does not explicitly state when to use this tool vs alternatives like 'list-datasets' or 'get-dataset-examples', nor does it mention conditions where it should not be used. The guidance is adequate but minimal.
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 full burden. It indicates a read operation and describes the return object ('A trace object with all spans that belong to the trace'). It does not discuss error handling, authorization, or side effects, but for a simple read tool it is adequate.
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 short, front-loaded with the core purpose, and includes a helpful example. Every sentence serves a purpose with no wasted words.
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 three parameters, zero schema description coverage, no output schema, and no annotations, the description covers the main functionality and return value but omits details about one parameter and error behavior. It is functional but not fully comprehensive.
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 add meaning. It explains 'project_identifier' and 'trace_id' implicitly via the example but does not mention the 'include_annotations' parameter. Two of three parameters benefit from the description, but one is entirely undocumented.
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 'Get a single trace by its exact trace ID within a project'. The verb ('Get') and resource ('trace') are specific, and the scope ('by exact trace ID within a project') distinguishes it from siblings like 'list-traces'.
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 includes an example ('Show me trace abc123def456 from project default') that implies when to use: when you have a specific trace ID. However, it does not explicitly state when not to use or mention 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, so the description must convey behavioral traits. It describes the return type and includes an example, but does not disclose pagination behavior (despite the 'limit' parameter), rate limits, authentication needs, or any side effects. The example is helpful but incomplete.
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: a brief one-line summary, then a clear definition of datasets, an example usage, and an expected return with formatted JSON. Every sentence adds value, and the key information is front-loaded.
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?
For a simple list tool with one optional parameter and no output schema, the description provides a clear example of the return object. However, it lacks explanation of the 'limit' parameter, error conditions, or how to handle empty results, leaving some gaps despite the low complexity.
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?
The input schema has 0% description coverage; the 'limit' parameter has no description in the schema and is not mentioned in the tool description. The description adds no meaning beyond the schema's type, default, and constraints. For a parameter with schema coverage 0%, the description should compensate but fails to do so.
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 gets 'a list of all datasets', provides a definition of datasets, and includes example usage and expected return with a sample JSON object. It effectively differentiates from sibling tools like 'get-dataset' by indicating it returns multiple datasets.
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 the purpose of datasets and provides an example query ('Show me all available datasets'). However, it does not explicitly state when not to use this tool or mention alternatives among the many siblings, which would enhance 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 bears the full burden. It discloses that the tool returns two content blocks: metadata and experiment data with results and evaluator annotations. This gives clear expectations about the output structure. However, it does not cover error states or behavior on missing IDs.
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 front-loaded with the core purpose, followed by details on return format and an example. It is reasonably concise (about 50 words) but could be tightened by removing the phrase 'for example, comparing the output...' which is explanatory but not essential.
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 simplicity (one param, no output schema), the description covers the purpose, return structure, and provides a concrete example. It does not address potential errors or edge cases, but for a read-only lookup, this level of detail is adequate.
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 one parameter experiment_id with 0% description coverage. The description does not add a formal parameter description but provides an example ID in the usage section, indicating the expected format. This partially compensates but does not fully explain constraints or valid values.
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 'Get an experiment by its ID.' It specifies the resource (experiment) and the action (get by ID), distinguishing it from sibling tools that list experiments or get other entities. The additional details about the return content solidify the purpose.
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 you have an experiment ID, e.g., 'Show me the experiment results for experiment RXhwZXJpbWVudDo4'. However, it does not provide explicit guidance on when to use this tool versus alternatives like get-dataset-experiments or list-experiments-for-dataset. No exclusion criteria or when-not-to-use are given.
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?
Describes return content (template, configuration, parameters) but no annotations provided; lacks details on authentication, rate limits, or error handling. Adequate but not rich.
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 short paragraphs with example usage and expected return. Every sentence adds value; no redundancy.
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?
Covers purpose, parameter, and return structure despite missing output schema. Could mention error cases or prerequisites but sufficient for a simple get operation.
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 coverage 0% and description only says 'using its version ID' with example value. No details on format, length, or constraints for the single parameter.
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?
Clear action 'get' and resource 'specific version of a prompt' using version ID. Distinct from siblings like get-latest-prompt and get-prompt-version-by-tag.
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?
Explicitly states 'using its version ID', implying when not to use (e.g., when you have a tag). However, no explicit when-not or alternatives mentioned.
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 behavioral burden. It discloses the return format with an example, mentions pagination via 'nextCursor', and describes that annotations can be created by humans, LLMs, or code. This is good behavioral context for a read-only 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, starting with the purpose, then a brief definition, followed by example usage and expected return. Every sentence adds value, and the structure is easy to follow.
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 6 parameters, no output schema, and no annotations, the description covers the core purpose and return format well. However, it lacks details on parameter usage and edge cases like missing spans or empty results.
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%. The description only indirectly references span_ids and project_identifier in examples, but does not explain include_annotation_names, exclude_annotation_names, cursor, or limit. Without schema descriptions, the tool's understanding of parameters relies heavily on examples, which is insufficient.
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 span annotations for given span IDs, explains what annotations are, and provides examples. It distinguishes from sibling tools like get-spans by focusing on annotations rather than spans.
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 gives example usage scenarios, showing how to call the tool with span IDs and a project. It implies when to use it (to get annotations) but does not explicitly mention when not to use it or offer 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, so description must disclose behavioral traits. It mentions expected return (prompt version object with template and configuration) but lacks details on side effects, authentication, or rate limits. Adequate for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with three short paragraphs: overview, usage with examples, and expected return. No superfluous content; each sentence serves a purpose.
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 simplicity and lack of output schema, the description covers essential usage and return value. Could note that prompt_identifier might be a name or ID, but examples imply this. Meets needs for a get-prompt tool.
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 coverage is 0%, so description adds meaning by explaining that prompt_identifier fetches latest version, and tag or versionId selects specific version. Examples further clarify usage, compensating for 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 action 'Get a prompt' and the resource, and distinguishes from siblings like get-latest-prompt and get-prompt-by-identifier by framing it as a single unified interface.
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?
Provides clear guidance on when to use tag or versionId to specify a version versus just the identifier for the latest version, with examples. Does not explicitly contrast with alternative sibling tools but context implies this is the primary getter.
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 provided. The description indicates it is a read-only support tool returning expert guidance, which is transparent about its behavior. No misleading statements.
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, front-loaded with the main purpose, uses bullet points for topics, and every sentence adds value without redundancy.
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?
For a simple single-parameter tool with no output schema, the description provides sufficient context: purpose, topics, when to use, and expected return. No gaps.
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%. The description repeats the schema's description for the 'query' parameter without adding new meaning, so baseline score of 3 applies.
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 'Get help with Phoenix and OpenInference' and lists specific topics (tracing, datasets, evals). It effectively distinguishes this support tool from sibling tools that perform concrete 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?
Explicitly says 'Use this tool when you need assistance with Phoenix features, troubleshooting, or best practices.' Does not mention when not to use, but the context of sibling tools makes it clear.
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:

[![phoenix MCP server](https://glama.ai/mcp/servers/Arize-ai/phoenix/badges/card.svg)](https://glama.ai/mcp/servers/Arize-ai/phoenix)

Score Badge

Copy to your README.md:

[![phoenix MCP server](https://glama.ai/mcp/servers/Arize-ai/phoenix/badges/score.svg)](https://glama.ai/mcp/servers/Arize-ai/phoenix)

Latest Blog Posts

Lightport: Open-Sourcing Glama's AI Gateway
By punkpeye on April 27, 2026.
open source
OpenAI
Tool Definition Quality Score (TDQS)
By punkpeye on April 3, 2026.
mcp
The Hackers Who Tracked My Sleep Cycle
By punkpeye on March 26, 2026.
security

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/Arize-ai/phoenix'

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