Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the effective side effect ('marking it as desired to be served') and that it avoids a new revision, plus the return shape, but is silent on permissions/authorization, idempotency, and reversibility for what is a mutation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose and usage are front-loaded in the first two sentences. The remainder is repetitive object-store boilerplate ('The output is automatically stored...', 'Use the returned object ID...') that restates the same idea multiple times and dilutes the signal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It covers purpose, usage, and return handling, but with no annotations and no output schema it should do more: it omits auth requirements and, importantly, fails to disambiguate from the sibling 'activate_deployment_revision', which is the biggest risk for this 1-param mutation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 for the single required parameter. ':param deployment_id: ID of the deployment.' merely restates the schema title ('Deployment Id') and adds no format, identifier source, or lookup guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Re-activates a deployment's current active revision') and even scopes it as the current active revision without pushing a new one. However, it never acknowledges or distinguishes itself from the near-identical sibling 'activate_deployment_revision', which an agent selecting between the two would need.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use it: 'to bring a deactivated deployment back online without pushing a new revision', which contrasts with a revision-pushing flow. No explicit when-not or named alternatives (e.g., deactivate_deployment, update_deployment) are offered, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It usefully discloses the return path (object ID like @obj_123 and object-store interaction), but for a mutation it omits key traits: whether activating replaces the currently served revision, required permissions, and reversibility/downtime implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded and the docstring params are compact, but the trailing block about the object store is repetitive ('object ID' repeated across three sentences) and bloats the definition with shared boilerplate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-param mutation with no annotations and no output schema, the definition covers the return/object-store behavior but leaves important gaps: effect on the currently served revision, auth requirements, and failure semantics. Adequate but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description is the only source of parameter meaning. It identifies both params ('ID of the deployment', 'ID of the revision to activate'), but these are near-tautological restatements of the names and add no format, constraint, or lookup detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (activates) and resource (revision) plus a concrete effect: 'marking it as the deployment's desired served revision.' This distinguishes it from the sibling activate_deployment (which targets a deployment, not a revision), though no sibling is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no reference to alternatives such as activate_deployment or create_deployment_revision. The agent must infer the calling context from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It usefully states the maximum tag count (three), the tag_name validation rules, and the return shape ('full tag list after the add, or error message'). However, it omits permissions/auth requirements, idempotency behavior for duplicate tags, and reversibility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core content is front-loaded reasonably well: purpose, limit, parameter notes, and return value. However, the trailing paragraphs about automatic object storage, preview object IDs, and object store tools are generic boilerplate that does not clearly earn its place for a simple tag-adding tool and adds noise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple mutation tool with no annotations or output schema, the description is fairly complete: it states what the tool does, the input constraints, the return value, and a deployment-level limit. It still lacks auth, error-condition, and duplicate-tag behavior, but covers the essentials an agent needs to call it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it largely does. It explains that deployment_id is the ID of the deployment and gives concrete character and length constraints for tag_name. It could go further by indicating where deployment_id comes from, but it meaningfully documents both parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence states a specific verb and resource: 'Adds a tag to a deployment.' It distinguishes the action from sibling tools like delete_deployment_tag and list_deployment_tags implicitly through the verb 'Adds.' However, it does not explicitly name or contrast against those siblings, so it falls just short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no when-to-use guidance, no alternatives, and no exclusions. The hard constraint of at most three tags is useful context but is a limitation, not usage direction. An agent is left to infer that this tool should be used whenever a tag needs to be added.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 usefully discloses default behavior (sizing defaults to DEVELOPMENT unless explicit sizing fields are given), the post-creation state (no active revision without a pipeline), and that output is auto-stored and returned as an object ID. It says nothing about permissions/auth requirements, provisioning side effects, or costs, which are meaningful gaps for a create-a-resource operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and the critical 'no active revision' caveat are front-loaded, and the param reference is justified by 15 undocumented params. However, the Examples block is generic boilerplate that references 'data' and 'threshold' – parameters that do not exist on create_deployment – which adds noise and potential confusion rather than value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 15-parameter mutation tool with no annotations and no output schema, the description is fairly complete: it covers parameter meaning, defaults, the revision workflow dependency, and the return/preview behavior (object ID usable with object store tools). It omits auth/permission prerequisites and any description of the created deployment's fields, but an agent has enough to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it does: a full :param list documents all 15 parameters with semantics and defaults (e.g., idle_timeout_in_seconds as 'Seconds of inactivity before scaling down', the MANAGED/SERVERLESS and PRODUCTION/DEVELOPMENT/CUSTOM enums, and the @obj_id reference syntax). Some entries are near-tautological (e.g., 'Name of the deployment', 'Maximum number of index replicas'), keeping it out of the top score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence gives a specific verb and resource ('Creates a new deployment (AI Gateway)') plus the scoping condition that it is optionally linked to a pipeline. An agent can distinguish this from siblings like create_deployment_revision, update_deployment, and deploy_pipeline. It stops short of explicitly naming which sibling to use in which situation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the key branch: without an origin_pipeline_id the deployment has no active revision until create_deployment_revision and activate_deployment_revision are called, which effectively routes the agent to the follow-up tools. It lacks explicit exclusions or guidance on when not to use create_deployment, but the workflow context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does well: it discloses that creation is non-serving and requires a separate activation step, and explains that the result is auto-stored and returned as a preview with an object ID. It omits permission/auth requirements, reversibility, and error conditions beyond a generic "or error message".

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and parameter notes are front-loaded and useful, but the description is padded with leaked Sphinx directives and a generic template block whose examples use parameters that do not exist on this tool (data, threshold). Those examples are actively misleading and do not earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-param mutation tool with no annotations and no output schema, the description covers purpose, the activation prerequisite, per-parameter meaning, the object-reference convention, and the return shape. Missing pieces are edge behavior such as auth requirements and validation failures.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it documents all five parameters including the source_type enum values and the situational role of config_yaml vs source_version_id. It also explains the @obj_id / @obj_id.path.to.value reference syntax, which the schema never mentions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Pushes a new revision onto a deployment") and immediately scopes it against the sibling that serves revisions. An agent can distinguish it from activate_deployment_revision without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly names the follow-up tool and the condition that selects it: creating a revision does not serve it, call activate_deployment_revision to serve it. No exclusions are given for when *not* to create a revision, so it stops short of a full when/when-not/alternatives statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it warns that replaying runs on customer models and requires a "yes" before creating when cost matters. It also explains the Recorded-vs-replayed asymmetry. It does not cover error behavior or rate limits, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well structured and front-loaded with purpose, then baseline semantics, cost warning, and parameter docs. It is somewhat long due to the embedded :param list, but every paragraph adds real information and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter tool with no output schema and no annotations, the description covers the creation semantics, baseline selection, cost implications, and id-discovery routing. It omits what the Experiment response contains beyond a brief :returns line, but overall it is complete enough to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it does: every parameter is explained, including the baseline/candidate pairing rules, the 1..50 range and default for sessions_per_test_case, and the replay_mode enum values. The only gap is that enum semantics for replay_mode are brief.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Creates an Experiment") and immediately scopes it as comparing a candidate pipeline version against a baseline. It explains what the tool is for in one sentence, which distinguishes it from siblings like list_experiments and get_experiment_results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear conditions for the baseline mode (set both baseline fields together vs. leave unset for Recorded) and names sibling tools for finding ids and reading results. It does not explicitly state when not to use the tool, but the routing information is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It does disclose useful behavior: the return is 'a message and the created index, or an error message', the output is auto-stored, and an object ID is returned for chaining. It says nothing about permissions, uniqueness enforcement on index_name, or side effects, which leaves real gaps 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded, but the body is bloated with Sphinx docstring artifacts (:param:, :returns:), generic boilerplate about the object store repeated twice, and example calls whose parameter names (data, threshold) do not match this tool's schema. Much of the text does not earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description does cover the return shape and object-store mechanics, which is the minimum needed to use the result. It omits duplicate-name behavior, required permissions, and what the YAML configuration must contain, so an agent could still call it incorrectly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 names all three parameters and adds genuinely useful object-reference syntax ('@obj_id.path.to.value') not present in the schema, but the per-parameter notes are thin (yaml_configuration is not explained beyond 'YAML configuration to use') and the code examples reference non-existent parameters (data, threshold), which is actively misleading.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence gives a specific verb+resource ('Creates a new index') scoped to the Haystack Enterprise Platform workspace, which is enough to distinguish it from unrelated siblings like create_pipeline or create_deployment. It does not explicitly contrast itself with the closest siblings (update_index, validate_index, deploy_index), so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use create_index versus update_index, validate_index, or deploy_index, nor any stated prerequisites. The only routing advice concerns post-call object handling ('use the object store tools'), not tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses output is automatically stored with object ID and returns a preview, which is useful. However, no annotations are provided, and the description does not cover potential side effects or authorization needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose front-loaded, but includes redundant docstring-style param/return lines and mismatched examples. Could be more concise and accurate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, 0% schema coverage, and many siblings, the description omits crucial context like YAML structure, prerequisites, error handling, and differentiation from pipeline versioning tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%. Description adds minimal info (pipeline name and YAML config), but the examples are mismatched, referencing non-existent parameters 'data' and 'threshold', which confuses. No explanation of YAML format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states verb 'creates' and resource 'pipeline' within deepset workspace. Distinguishes from siblings like create_index or create_workspace.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides examples and mentions object references for parameter passing, but lacks explicit guidance on when to use this tool versus alternatives like create_pipeline_version or deploy_pipeline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses immutability, version history, draft finalization, output storage, and object ID usage. No annotations, so description carries full burden and meets it thoroughly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with opening statement, behavior explanation, parameter list, and examples. Slightly verbose with redundant parameter docstring but not excessive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, all parameters, behavior, output format, and usage examples. Adequate for a 4-parameter tool with no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage. Description compensates fully by describing each parameter, default values, reference format, and providing examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it creates a new immutable version of an existing pipeline with YAML configuration. Distinguishes from siblings like create_pipeline and patch_pipeline_version.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage context: "Use this to update a pipeline's configuration." Explains behavior with is_draft and object references, but lacks explicit exclusions or when-not-to-use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. It discloses mutation (creates), uniqueness requirement, output format with object ID, and storage behavior. However, it does not mention permissions, rate limits, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise overall, but includes extra details about object store tools and referencing results, which are tangentially relevant. Could be slightly more streamlined.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers purpose, input, output, and uniqueness constraint. Lacks error handling details and authentication requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'name' has no schema description (0% coverage), but the description adds meaningful context: 'The name for the new workspace. Must be unique.' This compensates well for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Creates a new workspace with the specified name' and explains its role in organizing pipelines, indexes, and other resources. It clearly distinguishes from sibling tools like create_index and create_pipeline.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the uniqueness constraint and that workspaces can be used after creation, but does not provide explicit guidance on when to use this tool versus alternatives 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It adds useful behavioral context by stating the active revision is preserved (implying reversibility), but omits permissions/auth requirements, whether served traffic is immediately stopped, and whether the action can be undone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The lead sentence is well front-loaded and earns its place, but the trailing object-store boilerplate ('Use the object store tools...', 'Use the returned object ID...') is generic and largely irrelevant to a deactivate operation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-param mutation with no output schema and no annotations, the description covers the return path reasonably well, explaining the object-ID preview and how to consume it, though the safety/permission profile remains undocumented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single param is documented only as ':param deployment_id: ID of the deployment,' which restates the name. With one self-evident ID parameter the gap is minor, but the description adds no format or source detail to compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Marks a deployment as no longer desired to be served, keeping its active revision intact.' The 'keeping its active revision intact' clause implicitly distinguishes it from delete_deployment, though it never names that sibling directly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use guidance or alternatives. With siblings like delete_deployment, activate_deployment, and update_deployment in the list, the description never says when to prefer deactivation over deletion or how it relates to activation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully carries behavioral disclosure: it explains resumable snapshot behavior, the always-returned inline run trace, state-modifying dry_run substitutions, and the resume rejection on dry_run mismatch. It also notes that outputs are auto-stored and can be referenced via object IDs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Although long, the description is well structured: a clear overview, mode breakdown, parameter definitions, and examples. Each section adds essential operational detail, and the first sentence front-loads the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 9 parameters, no annotations, and no output schema, the description is remarkably complete. It covers return shape, side effects, dry_run behavior, error visibility, object-reference usage, and example calls, leaving almost no operational ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero descriptions, but the description documents all 9 parameters, including mutual exclusions, defaults (break_at_visit_count=0), ignored inputs when resume_from is set, and the requirement that pipeline_version_id requires pipeline_id. This fully compensates for the schema coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence states a specific action and scope: 'Runs a pipeline configuration in debug mode: pin a breakpoint, resume a snapshot, or trace a full run.' It also explicitly distinguishes from saved pipelines ('directly (not a saved pipeline by name)'), which separates it from siblings like run_component or deploy_pipeline.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description defines three mutually exclusive usage modes and tells users to pass YAML from get_pipeline_version/create_pipeline_version or a local draft. It provides concrete when-to-use guidance for dry_run ('Required for indexing pipelines... harmless for query pipelines') and warns to set it on resume too. However, it does not explicitly name alternative tools, so it stops short of full explicit exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It explicitly discloses the critical destructive trait ('along with all of its revisions. This cannot be undone.'), which is exactly what an agent needs. It could be improved by mentioning auth requirements or rate limits, but the core destructive behavior is well covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core message is front-loaded and clear, but the description is bloated with 6+ sentences of boilerplate about object storage ('The output is automatically stored...', 'Use the object store tools...'), which is irrelevant to a destructive delete operation and dilutes the signal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 required param, no output schema) and lack of annotations, the description covers the essential destructive warning but is incomplete on prerequisites and environment context. The included object-store boilerplate is noise for this operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the single parameter 'deployment_id', so the description must compensate. It does mention ':param deployment_id: ID of the deployment to delete.', providing a basic definition, but adds no format, syntax, or sourcing guidance beyond what the name implies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Deletes') and resource ('deployment') and clarifies the cascading scope ('along with all of its revisions'). This distinguishes it from siblings like update_deployment or deactivate_deployment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states it is irreversible but provides no guidance on when to use this tool versus alternatives like deactivate_deployment or delete_deployment_tag, nor any prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose one useful trait (case-insensitive tag matching) and the return shape. It omits important behaviors for a mutation: required permissions, idempotency (what happens if the tag is absent), and whether removal is reversible.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded, which is good, but the trailing block about automatic output storage, object IDs, and the object store is generic boilerplate that consumes roughly half the text without helping an agent call this delete tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter delete with no annotations and no output schema, the description covers purpose, both params, and the return value (full tag list or error message), which is adequate. It still leaves auth/permission requirements and failure modes unexplained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate; it does document both params via the docstring (:param deployment_id:, :param tag_name:) and adds the case-insensitivity rule for tag_name. However, the descriptions are minimal paraphrases ('Tag to remove.') rather than adding format or constraint detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Removes a tag from a deployment'), which is unambiguous. It does not explicitly distinguish itself from the close sibling add_deployment_tag or list_deployment_tags, but the verb makes the direction of the operation obvious.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no mention of the sibling add_deployment_tag/list_deployment_tags, and no prerequisites. The only guidance is the object-store boilerplate about passing the returned object ID to other functions, which covers follow-up usage rather than tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. It discloses that the tool attempts to deploy, returns validation errors on failure, and stores output with an object ID. However, it lacks details on prerequisites (e.g., index must exist) and implications of deployment.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is functional but somewhat verbose, with docstring-style param/returns and redundant mentions of object ID usage. It front-loads the main action but could be more streamlined.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the return format and error handling but omits important context: it mentions 'in the given workspace' but the schema lacks a workspace parameter. Missing details on deployment effects and prerequisites like validation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds a clear definition for the single parameter 'index_name' ('Name of the index to deploy'). This fully compensates for the missing schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool deploys an index to production, using a specific verb and resource. It distinguishes from sibling tools like create_index (creates but doesn't deploy) and validate_index (validates but doesn't deploy).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for deploying an existing index but does not explicitly compare to alternatives like create_index or validate_index. No guidance on prerequisites or when to choose this tool over siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses return behavior (validation result on failure, stored object ID) and how to use the object ID. However, it does not mention destructive actions, auth requirements, or side effects of deployment.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description includes docstring-style parameter and return sections, making it somewhat lengthy (6 sentences). Information is front-loaded, but could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, 0% schema coverage, and no output schema, the description provides parameter meanings, return value with object ID usage, and partial behavioral context. It lacks prerequisites (e.g., pipeline existence, permissions) but is fairly complete for deployment.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully explains both parameters: pipeline_name is the name of the pipeline, version_id is optional and defaults to latest non-draft version. This adds significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Deploys a pipeline to production,' which identifies the specific action and resource. It is distinct from sibling tools like 'create_pipeline' (which creates) and 'validate_pipeline' (which only validates).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide guidance on when to use this tool versus alternatives (e.g., deploy_index, validate_pipeline). It only explains behavior on failure but no context for appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description does not disclose any behavioral aspects such as side effects, permissions, or whether the operation is read-only. While the name suggests a get operation, the description does not explicitly state that it is safe or free of side effects, and there are no annotations to fill this gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably structured but contains redundant boilerplate about the object store and output formatting (e.g., repeating 'Use the object store tools...' twice). While not excessively long, this repetition could be trimmed for conciseness without losing essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, parameters, and return type, but it lacks context about why or when one would need a component definition (e.g., for inspecting inputs/outputs before pipeline creation). It does not explain the structure of the returned definition or its typical use in the broader workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides meaningful explanations for both parameters. For component_type, it gives a concrete example. For haystack_version, it explains how to derive the value from the pipeline's dependencies. This adds clarity beyond the schema, which only lists types and required status.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Returns the definition of a specific Haystack component.' This is a specific verb-resource pair, and it distinguishes from sibling tools like get_pipeline or list_pipelines by focusing on component definitions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide guidance on when to use this tool versus alternatives. It lacks any mention of use cases, prerequisites, or scenarios where this tool is appropriate, leaving the agent to infer its purpose from the name and generic description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so richly: sort defaults, pagination semantics (total across all pages, has_more, page_number), that total_requests counts queries not LLM calls, that a null cost_total means unpriced usage, the 30-day default window, that deleted pipelines still appear with a UUID label, and the required Pipelines read + Costs read permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core is well front-loaded: purpose, then the 'what drove the cost' framing, then parameter details. It is dense but nearly every sentence adds information; the trailing object-store boilerplate is the only portion that is generic filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, yet the description explains the return shape (key/label entries, total, total_requests, cost_total nullability) and the object-store handoff via an @obj_123 ID. An agent has everything needed to call the tool and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must document all eight parameters, and it does: group_by dimensions, pipeline_name scope plus its permission prerequisite, inclusive/exclusive date semantics with defaults, sort_by/sort_order options and defaults, limit range (1-100), and page_number starting at 1.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (reads LLM cost), names the scope (workspace or one pipeline) and the grouping dimensions. The phrase 'grouped by model, pipeline, deployment or API key' implicitly separates it from the totals and over-time siblings, but it never names them explicitly, so an agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use this to find what drove the cost: which models, pipelines, deployments or API keys spent the most' tells the agent exactly when this tool is the right pick. There is no explicit when-not guidance or named alternative (e.g. get_cost_totals, get_cost_over_time), so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it discloses that days without usage are omitted (missing day = zero cost), that cost_total null means usage existed but was unpriced, that unpriced calls are excluded from daily figures, and that pipeline_name requires Pipelines read plus Costs read.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose then semantics; each sentence about dates, nulls, and defaults earns its place. The trailing object-store boilerplate ('output is automatically stored... @obj_123... use the object store tools') is generic filler that dilutes an otherwise tight description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema or annotations exist, so the description must cover behavior and returns; it does, explaining the per-day return shape, null semantics, exclusions, date defaults, and permission requirements. An agent has everything needed to call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, yet all three parameters are documented: pipeline_name's scope and permission requirement, start_date as an inclusive ISO timestamp defaulting to 30 days before end_date, and end_date as exclusive with no default upper bound. This fully compensates for the empty schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (reads) and resource (LLM cost) with an explicit granularity (per UTC day) and scope option (workspace or one pipeline). This is clearly distinguishable from siblings like get_cost_totals (completeness check) and get_cost_breakdown (dimensional split).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives three concrete when-to-use cases (track spending over time, locate a spike's start day, compare periods) and routes the agent to get_cost_totals for verifying cost completeness. There is no explicit when-not guidance or contrast with get_cost_breakdown, so it falls short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so richly: it warns that total_cost may be a lower bound when is_cost_complete is false, explains unpriced_models and pending_generation_count (clearing within a minute), and notes total_requests counts queries not LLM calls. It also surfaces the required read permissions for the pipeline scope.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense and front-loaded with the core purpose, semantics, and caveats in the critical first lines. The closing object-store paragraph is somewhat boilerplate and repeated ('object ID' several times), but the informational paragraphs largely earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description explains the return path: a formatted preview with an object ID, that the output is auto-stored and referenceable, and that object-store tools can inspect nested properties. Combined with the cost caveats, an agent has everything needed to call and interpret it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate and it does: pipeline_name (restrict to pipeline, omit for workspace, permission requirement), start_date (inclusive ISO timestamp, defaults to 30 days before end_date), and end_date (exclusive, defaults to no upper bound), reinforced by a concrete September 2026 example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Reads the total LLM cost, tokens and requests') plus the scope ('of the workspace, or of one pipeline, over a date range'). This is distinguishable from implicit siblings like get_cost_breakdown and get_cost_over_time by naming 'totals'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly frames the use case ('Use this to answer how much a workspace or pipeline spent on LLM calls') and clarifies cost units (USD) and the workspace-vs-pipeline selection via pipeline_name. It does not name the alternative cost tools, so it stops short of full 5-level routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It transparently mentions that the output is automatically stored, returns an object ID, and can be referenced elsewhere. It also notes 'ComponentDefinitionList model or error message string.' It does not explicitly state read-only behavior, but the get semantics imply it. The disclosure of the object store side effect is an informative behavioral trait beyond what annotations would provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is organized with the main purpose first, followed by parameter documentation, return type, and additional usage notes. It is somewhat verbose but each sentence adds value, such as storage behavior and object ID usage. It is appropriately sized for a tool with one parameter and a clear output.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description fully clarifies what the tool returns (ComponentDefinitionList model or error string) and how to further interact with the result (object store tools). It covers all necessary aspects for a simple list tool, including the optional parameter handling and the storage mechanism. No critical information is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates by explaining the parameter 'haystack_version' in detail: 'Pass the same version as specified in the pipeline yaml's `dependencies` section.' This adds meaning beyond the schema, which only shows the type. The explanation is sufficient for understanding how to set it correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get a list of all installed custom components.' This is a specific verb ('Get') and resource ('list of installed custom components'), and it distinguishes from siblings like list_custom_component_installations, which focuses on installation logs. The purpose is unambiguous and effectively differentiates from similar tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('get a list of all installed custom components') but does not explicitly state when to prefer this tool over alternatives like search_component_definitions or get_component_definition. There is no guidance on exclusions or comparisons, though the purpose is clear. It gives basic instructions on the optional haystack_version parameter but lacks explicit when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose an important non-obvious behavior: the result is stored in an object store and returned as a formatted preview with an object ID. However, it says nothing about permissions, error conditions beyond 'error message', or whether the read is side-effect free.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded and clear, but the docstring-style block is padded: ':returns: Deployment details or error message' is followed by three sentences that all circle the same object-store/object-ID idea, with the last two largely redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description is right to describe the return mechanism (object ID, preview, chaining into object-store tools). It stops short on other essentials such as error handling and where the deployment_id comes from, leaving gaps for a tool embedded among ~60 siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the single param has no schema description, so the description must compensate. It offers only 'ID of the deployment to fetch', which restates the parameter name rather than clarifying format, source, or where to obtain it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Fetches details for a specific deployment') and adds the scope of what is returned (active revision, runtime status). This distinguishes it from list_deployments and get_deployment_revision without naming them, but the differentiation is implicit rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description never says when to use this tool versus the many siblings (get_deployment_revision, get_deployment_stats, get_deployment_metrics, list_deployment_revisions). Usage is only inferable from 'specific deployment', so an agent gets no routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It does disclose a real behavioral trait: output is auto-stored and returns an object ID (@obj_123) that feeds object-store tools. That is valuable and non-obvious. It does not, however, cover auth/permission requirements, read-only nature, 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded, but the object-store explanation is repeated across three sentences ('referenced in other functions', 'use the object store tools', 'pass this result to other functions') with overlapping meaning. The docstring-style ':param/:returns' formatting is also less scannable than prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations and no output schema, the description does cover parameter meaning and the return/object-store mechanism, which is the critical missing structured data. It falls short on usage routing relative to get_deployment_stats and on any access/permission context, leaving an agent with gaps before invoking it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides only bare titles, and the description compensates by documenting all three parameters, including the unit for start_ms/end_ms ('unix milliseconds'). That unit detail is genuinely additive. Deployment_id is described only generically ('ID of the deployment'), so it is not perfect.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence gives a specific verb and resource: 'Fetches per-replica CPU/memory usage, resource limits, and replica counts for a deployment.' An agent immediately knows the resource and the metric types returned. However, it does not distinguish itself from the sibling get_deployment_stats, which an agent could plausibly confuse with it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance and no mention of alternatives or prerequisites. With siblings like get_deployment_stats and get_deployment present, the description should state what selects this tool over those, but it leaves that entirely to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, but it does disclose real behavior: the result is auto-stored, returned as a formatted preview with an object ID, and nested properties are reached via object-store tools. It omits permission/auth requirements and only vaguely gestures at errors ('or error message').

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The lead sentence is well front-loaded, but the trailing block repeats the object-store idea across four sentences ('automatically stored', 'referenced in other functions', 'use the object store tools', 'pass this result to other functions') without adding new information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-param fetch with no output schema, the description covers the return shape (revision + config YAML) at a high level and explains the object-store handoff. It is missing selection guidance and the meaning of the two IDs, which are the main gaps an agent would hit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, yet the :param lines merely restate the names ('ID of the deployment', 'ID of the revision to fetch') without format, source, or how to obtain them. It adds almost no meaning beyond the parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Fetches a single deployment revision') and adds scope detail ('including the pipeline configuration YAML it serves'), which clearly separates it from the sibling list_deployment_revisions. An agent can identify the operation without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to choose this over siblings like get_deployment, list_deployment_revisions, or activate_deployment_revision, and no prerequisites or exclusions. The object-store notes describe what to do with the result, not when to call this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does add genuinely useful behavioral context: the result is auto-stored in an object store and returned as an object ID (@obj_123) that can be fed into other functions and inspected via object-store tools. What it omits is the read-only nature, any authorization requirements, and whether values are nil for inactive deployments.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The :param/:returns block is efficient, but the object-store tail is spread over three sentences that partially restate each other ('stored and can be referenced', 'use the object store tools', 'pass this result to other functions'). Front-loading is good, but there is redundancy that could be compressed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-param tool with no output schema and no annotations, the definition covers parameters well and explains the important non-obvious behavior that the actual data lives behind an object ID. It is nearly complete; the remaining gap is distinguishing it from the metrics sibling and any dependency on deployment state.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it largely does: each of the four params gets a :param line with units, bounds (1-90), defaults (30, DAY, UTC), and the enum values for granularity. This is meaningfully richer than the bare schema titles, though it doesn't explain time_zone's effect on bucket boundaries beyond 'bucket the series'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource and enumerates exactly what is returned: query volume, outcome split, and run durations for a deployment. That is a clear purpose. However, it never distinguishes itself from the sibling get_deployment_metrics, which an agent could easily confuse with stats, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites (e.g., the deployment must be active), and no reference to alternatives such as get_deployment_metrics or get_deployment. The agent is left to infer applicability purely from the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the burden, and it discloses real behavioral traits: tries expire after an hour (TTL), an EXPIRED state exists, and it may return 'an error message'. It doesn't cover auth or idempotency, but the lifecycle and failure semantics are genuinely useful beyond structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose and expiration behavior, then the params. The Sphinx-style :param:/:returns: tags are slightly redundant with the prose but compact and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly states the return ('The try report, or an error message'), and both required params are covered. For a two-param read tool this is essentially complete, with only auth/prerequisite detail missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it documents both parameters. Critically, it explains that try_id originates from try_evaluator, adding meaning the bare string-typed schema does not convey.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Reads') and resource ('an ad-hoc try'), and ties it directly to the sibling try_evaluator that produced the try. An agent can distinguish it from try_evaluator (which initiates) without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly establishes the workflow: try_id comes 'from try_evaluator', so this is the retrieval step after a try is handed back. It also gives a concrete remedy on EXPIRED (re-run try_evaluator). It lacks an explicit 'do not use this to start a try' exclusion, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden, and it does a good job: it discloses exactly what is returned in each mode, that source is omitted unless version_id is set, that errors come back as a message, and the object-store side effect with an @obj_123 ID. It does not cover permissions or rate limits, hence not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence is well front-loaded, but the piece is padded: the :param version_id line restates the opening paragraph, the :returns line restates the outcome, and the closing two sentences about the object ID duplicate the earlier object-store note. Structure is conventional but several sentences do not earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must explain return values, and it does so for both modes plus the object-store preview and cross-function reuse. For a two-parameter read tool this is nearly complete; only permission/error-handling detail is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it does: both parameters are documented via :param lines, with version_id carrying real behavioral meaning (it controls whether python_code is returned). evaluator_id's semantics as "The Evaluator's id" is thin but adequate alongside the required-parameter constraint.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Fetches one Evaluator, or one of its versions with the Python source") and immediately distinguishes the two operating modes by the presence of version_id. It implicitly contrasts with the list_evaluators sibling via "one Evaluator," but never names the alternative explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete conditional guidance: without version_id you get every version newest-first without source; with version_id you get that version's python_code, and it tells you to pass latest_version.evaluator_version_id to read current source. No exclusion or explicit alternative tool is named, but the when-to-use context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so richly: it discloses that results are partial while status is REPLAYING or SCORING, that 'errored' means the Evaluator's own code broke, that Metrics are averaged only over paired_test_cases with values on both sides, and that pipeline_metrics compares duration/tokens/cost. These are exactly the semantic gotchas an agent could not infer from the bare schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose, then structured into metrics/pipeline_metrics/test-case detail; nearly every sentence carries information. Minor deduction for the generic object-store boilerplate tail that is auto-injected and adds little selection value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description thoroughly describes the return shape (per-side value/labels, delta, delta_pct, errored counts, per-Metric means, session listings) so an agent knows what it gets. Nothing material for correct invocation appears to be missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it largely does: max_test_cases is explained precisely ('Most test cases to return; metrics still cover every test case'), which is a non-obvious interaction. experiment_id is only described as 'The Experiment's id,' which is thin but sufficient given the required string.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Reads an Experiment's results') and immediately enumerates the scope it covers: each Metric, candidate vs baseline, and each test case. It is clear and concrete, though it never explicitly distinguishes itself from the nearby sibling get_experiment_run, leaving that routing decision to the agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives actionable context: find the Experiment via list_experiments, resolve evaluator ids via list_evaluators, and check paired_test_cases counts before trusting a delta. It even points the agent at where to look for regressions. It stops short of explicit when-not-to-use or exclusion conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it distinguishes 'errored' (Evaluator code breaking, see error_detail) from 'failed' (never ran, retryable), explains that summaries cover rows past max_rows, and warns that only SAMPLED runs generalize to traffic. It omits any auth/permission or rate-limit context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Long but dense and front-loaded: the first sentence states the return shape, and each subsequent sentence earns its place by explaining semantics rather than restating the name. The trailing object-store/object-ID boilerplate is templated and slightly padded, but still operationally relevant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex, no-annotation, no-output-schema read tool this covers the important ground: return structure, error-vs-failure semantics, summarization scope, and the selection caveat. Missing only ancillary operational details such as permissions and payload size limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 4 params, so the description must compensate. It meaningfully clarifies max_rows ('Most rows to return; the summaries still cover every row') and gives terse docstrings for the other three, but adds no format or sourcing detail beyond the identifiers' identity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb+resource ('Reads one scoring run') and then enumerates the exact return shape — cell outcomes, per-Metric summary, grid rows. It also defines what a scoring run is, which lets an agent distinguish it from run-adjacent siblings like get_evaluation_try or get_pipeline_trace.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly routes the agent to the tools that produce the required identifiers ('Find one with list_metric_groups (last_run) or list_experiments (a side's scoring_runs)'), which is genuine when-to-use guidance. It stops short of stating when-not-to-use this tool or any exclusion conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the return shape ('String representation of the object or of the value at path'), which is valuable given there is no output schema. But it omits failure behavior (missing id, invalid path), any permission/auth requirements, and any side-effect statement for this 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then progressively adds the id mode, the path mode, and parameter docs. The JSON example is worth its space; the format is efficient overall, with only minor redundancy between the prose path explanation and the `:param path:` line.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description covers the essentials (what it returns, both parameter formats), which is more than minimal. But it stops short of error/edge-case behavior and any resource-identification guidance for deciding between this and the other object-store tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must carry parameter meaning, and it does: it specifies the id format (`@obj_001`) and two accepted path syntaxes (`access.to.attr` and `["access"]["to"]["attr"]`) with a concrete example. This meaningfully compensates for the undocumented schema, though the empty-string default for `path` is never explained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('fetch an object from the object store') and immediately operationalizes it with two retrieval modes. It is clear on its own, but never names the adjacent siblings (get_slice_from_object_store, sed_object_store, grep_object_store) to draw the boundary, so an agent still has to infer which store tool applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the two ways to call it (by id, or by id plus nested path with a worked example), which is genuine usage guidance. However, it gives no when-to-use vs the many store siblings and no exclusions, so the routing decision is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It usefully discloses that output is auto-stored, returned as an object ID preview, and that nested properties require object store tools, but says nothing about auth requirements, read-only nature, or error conditions beyond 'an error message'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded, but the entry mixes Sphinx-style ':param:'/':returns:' directives with prose and then spends three sentences on object store mechanics. Some redundancy between ':returns:' and the object-ID paragraphs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description does the needed lifting by describing the return value ('index configuration or an error message') and the object-ID indirection pattern. It is nearly complete for this simple one-parameter read.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single parameter has no schema description, so the description must compensate. It does document index_name as the 'Unique name of the index to fetch', which adds the uniqueness constraint but no format or naming convention details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Fetches') and resource ('detailed configuration information for a specific index'), anchored by the unique index_name. It is clearly differentiated from list_indexes by the singular scope, though no sibling is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use vs. list_indexes/update_index guidance, but the description does give downstream workflow direction ('Use the object store tools in combination with the object ID'). Usage is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that the output is stored and returns an object ID for further use, implying a read operation. However, with no annotations, it does not explicitly confirm no side effects or state the exact behavior beyond output handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is adequate but contains some redundancy (e.g., mentioning logs twice). It is structured as a paragraph but could be more streamlined.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters or output schema, the description sufficiently explains the output format and how to use it with object store tools. It lacks details on error scenarios but covers the essentials.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters are defined, so the schema provides full coverage. The description adds value by explaining the output storage and object ID usage, meeting the baseline for a zero-parameter tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves logs from the latest custom component installation. The name and description are consistent, but no explicit differentiation from sibling tools is provided.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes instructions on using the output with object store tools but lacks guidance on when to choose this tool over alternatives like list_custom_component_installations or get_custom_components.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and provides good transparency: it states the tool lists models (read-only), returns a page of models with an object ID, explains output storage and how to access nested properties, and mentions error messages. It does not cover rate limits or page size limits but is otherwise thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose paragraph, parameter documentation, and output handling. It is front-loaded and each sentence adds value, though some redundancy exists (e.g., repeating 'configuration options'). Overall, it is appropriately sized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no required fields, no output schema, and no annotations, the description is highly complete. It explains all parameters, output format (object ID), how to use the object store, error messages, and the scope (workspace/organization level). No important details are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates by providing detailed explanations for all 5 parameters: limit, page_number, connected, provider (with well-known values and case-insensitivity), and model (with substring match). Each parameter's behavior is clearly explained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists models including configuration options, covering predefined and custom models, and specifies its use case for discovering model names, providers, and default configurations for chat generators. It distinguishes itself from sibling tools that handle indexes, pipelines, or workspaces.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('Use this tool to discover...'), providing clear context. However, it does not explicitly mention when not to use it or alternatives, though no competing sibling tool exists.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It discloses that the output is automatically stored, returns a formatted preview with an object ID, and can be passed to other functions. It also mentions error messages. This is valuable beyond the schema, though it stops short of stating permissions or explicit read-only safety.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately sized but contains redundancy; two sentences about using the object ID could be merged. The use of `:param` and `:returns` is conventional, but the instructions are somewhat scattered.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool, it covers the return format and how to use the object ID, but it lacks explicit usage guidance versus siblings and does not elaborate on error conditions beyond a generic message. The overall context is adequate for basic use but not comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at 0%, the description must compensate. However, it only restates the parameter name ('The name of the pipeline to fetch') and adds 'unique', which adds little meaning beyond the schema. No format, constraints, or examples are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it 'Fetches information for a specific pipeline' and identifies the resource by the unique `pipeline_name`, distinguishing it from sibling tools like `list_pipelines` (fetching all) and `get_pipeline_version` (a version-specific resource).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It implies usage when you have a specific pipeline name, but does not explicitly mention when to prefer this over alternatives, nor does it state exclusions. The guidance about using object store tools is about post-processing, not tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It explains output is automatically stored and returns an object ID, which is key behavioral info. It could be more transparent about permissions or format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a front-loaded purpose and parameter docs, but has slight redundancy between 'Fetches logs' and 'Retrieves log entries'.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It covers parameters and output usage but lacks details on return format and pagination mechanics (how to get the first cursor). Given no output schema, this is a gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by explaining each parameter's purpose (pipeline_name, limit, level, after) with clear semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Fetches logs for a specific pipeline' with a specific verb and resource. It distinguishes from siblings as no other tool fetches logs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions 'useful for debugging pipeline issues or monitoring pipeline execution', providing clear context. However, it lacks explicit exclusions or alternative comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and discloses important behaviors: returns all spans, excludes component input/output, includes timing and failure details, and automatically stores output with an object ID. It clearly signals a read-like operation via 'Retrieves' and explains the side effect of storage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than strictly necessary, with some redundancy (query_id source is mentioned twice). However, it is well-structured with a front-loaded purpose, clear param block, and separate usage guidance. Every major section earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of annotations and output schema, the description provides a comprehensive context: what is returned, what is excluded, error handling, object store integration, and how to reference the result. It covers the tool's role in the broader workflow and directs users to related tools when needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no descriptions in properties), but the description fully explains both parameters. pipeline_name is simply 'Name of the pipeline,' and query_id is given a detailed definition with instructions to obtain it from the query_id/search_history_id field of list_pipeline_traces or list_pipeline_search_history responses.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Retrieves the Haystack pipeline run trace including all spans for a single search history record.' It clearly differentiates from sibling tools by focusing on the full trace for a single query, and mentions alternatives for other granularities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('Use this to deep-dive into a specific query run') and how to obtain required identifiers ('obtainable from list_pipeline_traces or list_pipeline_search_history'). It also provides concrete alternatives: 'For a targeted look at one span... use get_pipeline_trace_span_tags; for the logs, use get_pipeline_trace_logs.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the return structure (logger, level, message, timestamp, extra fields), the exclusion of errors outside the pipeline, and the automatic storing of output with an object ID for reference. It does not mention pagination or auth, but provides substantial behavioral context beyond a simple read-only claim.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but well-structured with clear paragraphs and param documentation. Some redundancy exists around the object ID mention ('Returns a formatted preview' and 'Use the returned object ID'), but the content is all relevant and earned.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's 2 parameters, no output schema, and no annotations, this description is remarkably complete. It covers purpose, use cases, parameter semantics, expected return format, error boundaries, related tools, and how to reference the result. It leaves little ambiguity for an AI agent attempting to invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the input schema provides no descriptions for either parameter (0% coverage), the description compensates fully by defining pipeline_name as 'Name of the pipeline' and query_id as 'UUID of the search history query', and additionally tells users where to obtain query_id. This exceeds schema-level information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Retrieves the log entries for a single Haystack pipeline run trace', which is a specific verb+resource combination. It distinguishes from siblings by naming the alternative get_pipeline_logs and explaining when each should be used.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly says when to use this tool (to diagnose warnings/errors emitted during a run) and when to use get_pipeline_logs instead (pipeline startup errors, configuration issues). It also explains how to obtain query_id from list_pipeline_traces or list_pipeline_search_history.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the return type (tag dictionary or error message), the automatic storage of output, and the object ID behavior. It lacks details on permissions or rate limits, but covers key behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with clear sections (purpose, usage hints, param docs, returns) and uses formatting (bold, code) for readability. It is somewhat lengthy but each section adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage context, parameter semantics, return behavior, and object store integration. Given no output schema or annotations, it is fairly complete, though it could include an example of the tag dictionary structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description explicitly defines each parameter in the :param: lines, including types and purpose (e.g., 'UUID of the span whose tags to retrieve'). This compensates well for the schema's silence.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Retrieves all tags for a single span') on a specific resource ('within a Haystack pipeline run trace'), clearly distinguishing it from sibling tools like get_pipeline_trace.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context ('Use this to inspect one component run in detail') and explains how to obtain the required parameters from other tools (get_pipeline_trace, list_pipeline_traces). It does not explicitly mention when not to use it, but the guidance is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses that the output is automatically stored in the object store and returns an object ID for referencing. It explains how to use the result with object store tools. However, it omits authentication needs, rate limits, or side effects, though the operation is likely read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative with a clear purpose, parameter docs, and output usage notes. It is slightly lengthy due to repeating param docs that could be in the schema, but overall front-loaded with the main action. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple fetch tool with no output schema, the description explains the return format (object ID) and how to use it. It covers the basic workflow but lacks details on error handling or prerequisites. Given the tool's simplicity, it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description includes explicit parameter documentation: ':param pipeline_name: Name of the pipeline.' and ':param version_id: UUID of the version to fetch.' This adds crucial meaning beyond the schema's property titles.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it fetches a specific pipeline version by ID, using the verb 'fetches' and specific resource 'version'. It distinguishes from siblings like 'list_pipeline_versions' (which lists all versions) and 'patch_pipeline_version' (which modifies).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (fetching a specific version) but does not explicitly guide when to use this vs. alternatives like 'list_pipeline_versions' for multiple versions or 'get_pipeline' for the pipeline itself. It mentions using object store tools for nested properties, which helps for further interaction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavior. It states the secret value is not returned, output is stored, and an object ID is returned for referencing. It lacks details on auth needs, rate limits, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise but contains redundant phrasing (e.g., 'Use this tool to get information about a specific secret' reiterates the purpose). It front-loads the core action but could be tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no annotations, the description covers retrieval, security nuance, and output format. However, it omits specifics like what metadata is returned, error case handling, and explicit object store usage instructions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%. The description adds minimal semantics by calling secret_id 'the unique identifier of the secret to retrieve,' which is a slight improvement over the schema's bare title. Given the low coverage, more elaboration is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the function: 'Retrieves detailed information about a specific secret by its ID.' It differentiates from siblings like list_secrets by emphasizing 'specific secret' and 'by ID'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use: 'Use this tool to get information about a specific secret when you know its ID.' It also notes that the secret value is not returned for security reasons, implying alternative uses for other data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It discloses the return payload (replay report with the new Session's id) and an error case, which is useful behavioral context, but omits permission requirements, whether the call is read-only/safe to retry, and how it behaves while the replay is still running.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Compact and front-loaded with the core action and the sibling linkage first. The reST directive fragments are a little terse but each line adds information; nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations and no output schema, the description covers the essentials: what it returns (replay report plus Session id), the error case, and both parameters. Reasonably complete for a 2-param read tool, with only permission/retry semantics left implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It documents both parameters, notably giving provenance for ``replay_run_id`` ('from ``replay_session``'), which is meaningful beyond the bare schema titles. It doesn't specify the id format or pipeline_name constraints, keeping it short of a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Reads') and resource ('a session replay'), and ties it directly to the sibling ``replay_session`` that generates the id. It clearly distinguishes itself from the tool that starts the replay, though the phrase 'handed back while it was still running' is slightly awkward.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (retrieve the replay produced by ``replay_session``) and notes the return depends on the replay having ended, but never explicitly states when to call this versus ``replay_session`` or what to do if the replay hasn't finished.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description indicates the return type (string representation) but does not disclose error handling (e.g., invalid indices) or side effects. With no annotations, more details are expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and includes a structured docstring format, though the parameter tags are slightly verbose for a tool description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately covers the tool's functionality and parameters, but lacks information on error cases and does not differentiate from sibling tools in the object store family.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters are explained with purpose and defaults/optionality, adding significant value since the input schema has 0% description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (extract) and resource (slice from object store), which is distinct from siblings like get_from_object_store (whole object) and grep_object_store (search).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 such as get_from_object_store or grep_object_store; usage context is only implied by the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even without annotations, the description discloses key behavioral traits: the output is automatically stored, a preview with an object ID is returned, and the ID can be used with object store tools or passed to other functions. This goes beyond just 'fetches template' and aids the agent in understanding the return format and side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: main purpose first, then param explanation, return behavior, and usage tips. The docstring-style formatting is clear, and each sentence adds value. Slight room for improvement by front-loading more critical info.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple get-template tool with one parameter and no output schema, the description covers purpose, parameter, return value (including object ID utility), and error handling (indicated by 'error message'). This is sufficiently complete for the complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so the description must compensate. It provides a basic docstring for the only parameter, 'template_name', stating 'The name of the template to fetch.' This adds meaning beyond the schema (which only gives title and type), but lacks details on format, allowed values, or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the action ('Fetches detailed information') and the resource ('specific pipeline or indexing template'), identified by a unique name. It clearly distinguishes from sibling tools like list_templates or search_templates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool (to get details of a single template by name), but it does not explicitly state when not to use it or point to alternatives (e.g., list_templates for listing, search_templates for searching). No direct comparison to siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 describes the output format (object ID, preview), explains how to access nested properties via object store, and mentions error messages. It implies a read-only operation, but does not explicitly state non-destructive behavior. Overall, it is transparent for a simple get operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, front-loading the purpose and then detailing output and usage. It is slightly verbose with repetition (e.g., 'This tool retrieves' and 'Use this when'), but every sentence adds value. The docstring-style parameter description is included.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and a simple single-parameter input, the description provides comprehensive context: return format (object ID, preview), instructions for accessing nested properties via object store, and mention of error messages. It fully compensates for missing structured metadata.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description adds meaning by stating 'The name of the workspace to fetch details for.' This clarifies the parameter's purpose beyond the schema's title 'Workspace Name' and indicates it is required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches detailed information for a specific workspace by name. It uses a specific verb ('Fetches') and resource ('workspace'), and distinguishes from siblings like list_workspaces (list all) and create_workspace (create).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear context for when to use the tool: 'Use this when you need detailed information about a particular workspace.' It does not explicitly exclude alternative tools like list_workspaces, but the purpose is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool returns matches with surrounding context (similar to grep) and explains each parameter's role. It does not mention side effects, authentication, or rate limits, but the behavior is clearly described as a read-only pattern search with optional context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, starting with the main purpose and then listing parameters. It is front-loaded and each sentence adds value. The parameter list is slightly redundant with the schema but aids readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, 2 required, no output schema), the description is complete. It explains input format, optional parameters, and return value (matches with context or no-matches message). No gaps are apparent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description adds meaningful explanations for each parameter: 'object_id' format (@obj_001), 'pattern' as regex, 'path' as navigation to nested attribute, and 'case_sensitive' default. This significantly enhances the schema's minimal definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: searching for a regex pattern in a string stored in the object store. It uses a specific verb ('search for') and resource ('string stored in the object store'), and distinguishes itself from sibling tools like 'get_from_object_store' and 'sed_object_store' by mentioning 'regex pattern' and 'similar to grep'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (search for patterns in object store strings) and includes optional parameters (path, case_sensitive) that guide usage. It does not explicitly state when not to use it or compare to alternatives, but the provided context (e.g., 'similar to grep') implies appropriate use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full transparency burden. It discloses that the output is automatically stored and returns a preview with an object ID for further reference, which is a behavioral trait beyond the schema. It also notes the return type (ComponentFamilyList model or error message). While it doesn't explicitly state read-only or error conditions, the 'lists' verb implicitly indicates a safe read operation, and the storage behavior is disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by parameter and return information, then a short paragraph on output usage. It uses a structured format with :param and :returns tags, which aids scannability. While a bit lengthy, each sentence provides useful information without redundancy, earning a high score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 covers all necessary aspects: purpose, parameter semantics, return type, and the handling of returned object IDs. It also explains that the output is stored for later use, which is essential for the agent to understand the tool's behavior. No significant gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter (haystack_version) is thoroughly explained: 'Optional version of Haystack to use. Pass the same version as specified in the pipeline yaml's `dependencies` section for the `haystack-ai` package.' This adds crucial context beyond the raw schema, which only declares a nullable string. The description compensates fully for the 0% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Lists all Haystack component families that are available on deepset.' The verb 'lists' plus the resource 'Haystack component families' makes the purpose specific and unambiguous. It also distinguishes itself from sibling list tools (e.g., list_workspaces, list_pipelines) by targeting a unique resource category.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides contextual guidance for the optional haystack_version parameter and explains how to use the returned object ID. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions. Usage is implied rather than stated, fitting the 'implied usage' criterion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses the return format (formatted preview with object ID) and mentions automatic storage for cross-function reference. However, it lacks details on error handling 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise but includes a Python-docstring style for parameters that adds length without improving clarity for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers pagination, return format, and object ID usage, compensating for lacking output schema. However, it omits error conditions and authorization requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description explains 'limit' as max per page and 'after' as cursor for pagination, adding meaning beyond the schema's type and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'List custom component installations' as the action and resource, which is distinct from sibling tools like 'list_component_families' or 'list_indexes'. However, it does not explicitly differentiate from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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, missing exclusion criteria or context about pagination limits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does add a genuinely useful behavioral trait: output is automatically stored, returned as a preview with an @obj_ ID, and can be fed to object-store tools or other functions. However, it says nothing about permissions, result limits, or event types beyond 'activation and revision activity'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose leads, but the body mixes docstring-style :param/:returns tags with prose and then repeats the object-store workflow across three sentences. It is usable but padded, with some redundancy about passing the object ID to other functions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-param, no-output-schema, no-annotation tool, the description does cover the return shape (paginated activity events/preview) and the object-store handoff, which is the main gap. It is nonetheless thin on parameter meaning and on how the activity list relates to the deployment revision tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 name both params, and 'after: the cursor to fetch the next page' usefully explains the opaque anyOf/null cursor field, but 'deployment_id: ID of the deployment' is tautological and adds nothing over the schema title.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — listing activation and revision activity for a deployment — which is distinct from revision-listing siblings like list_deployment_revisions. It does not explicitly name or contrast with those siblings, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance and no condition that selects this tool over list_deployment_revisions, get_deployment_revision, or get_deployment_stats. The reader must infer the use case from the purpose sentence alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose useful behavior: results are paginated via a cursor and the output is auto-stored as an object with a preview ID. However, it omits ordering, page-size limits, and permission requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is front-loaded and efficient, but the Sphinx-style param lines duplicate the schema and the four lines of object-store boilerplate add notable length. Structure is reasonable but not tight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description still covers both parameters, the paginated return shape, and the object-store handoff mechanism. It is largely complete for a two-parameter list tool, missing only ordering and permission details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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, and it does document both parameters meaningfully ('ID of the deployment', 'cursor to fetch the next page'). It adds the needed semantics the bare schema lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Lists') and resource ('revisions of a deployment') and expands it concretely as 'the history of pipeline configurations it has served.' This cleanly distinguishes it from the singular sibling get_deployment_revision and the related create/activate revision tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through the pagination cursor note and the object-store instructions, but the description never states when to prefer this over get_deployment_revision or list_deployments, nor any prerequisites. There is no explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does disclose key behavior: it is a read-style listing, results are paginated by cursor, and the output is stored in the object store with a preview object ID. It omits permission/auth requirements and any rate or scope limits, but the operational model is largely covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and parameters are front-loaded and useful, but the tail repeats itself: 'output is automatically stored and can be referenced in other functions' is restated by 'Use the returned object ID to pass this result to other functions,' so the final block does not fully earn its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description compensates by explaining the return shape (paginated list or error, formatted preview with @obj_123) and how to consume it via object store tools. Minor gaps remain on error cases and scope of the listing, but an agent has enough to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description documents all three parameters: after is the next-page cursor, group_label filters by group label, and tags filters with 'any tag present' semantics. That 'any match' clarification is meaning the schema alone could not convey.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Lists deployments (AI Gateways)') plus the scope ('currently configured deepset workspace'), and adds a definition of what a deployment is. It distinguishes itself reasonably from siblings like get_deployment or list_deployment_revisions, though it never explicitly contrasts them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: the filter parameters (group_label, tags) and cursoring suggest browsing/filtering a deployment inventory, but the description never says when to use this instead of get_deployment or create_deployment, nor states any prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It conveys that this is a read (list) with a return shape plus a generic note that output is stored and referenceable via an object ID, which is genuinely useful. It does not disclose permission requirements or rate-limit behavior, and the object-store boilerplate is copy-pasted rather than tailored.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening purpose and the :param/:returns lines are tight and front-loaded. However, four lines of generic object-store boilerplate ('The output is automatically stored...', '@obj_123', 'Use the object store tools...') are repeated filler that does not clearly apply to a tag-name list and dilutes the signal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter list tool with no output schema, the description covers purpose, both params, and return type, which is adequate. But with zero annotations the behavioral profile (authorization, whether tags are workspace-scoped) is unstated, and the mismatched object-store text leaves the actual return contract ('list of distinct tag names or error message') ambiguous.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must document both parameters, and it does. 'name' is described as a case-insensitive substring filter on tag names, and 'order' is documented as ASC/DESC with default 'ASC' — semantic detail beyond the bare schema. Only a minor gap remains (no note that the default means omitted order sorts ascending).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Lists distinct tag strings attached to deployments in the workspace.' An agent can distinguish it from list_deployments or add_deployment_tag by the 'distinct tag strings' scope. It stops short of explicitly naming a sibling to route against, so it is clear rather than fully differentiating.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Adds usage context ('useful for autocomplete when adding tags, or to see how deployments are currently grouped'), which implies when it is helpful. However it never names the relevant alternatives (add_deployment_tag, delete_deployment_tag, list_deployments) or states exclusions, so routing is left partly to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden; it discloses that this is a read-only enumeration returning sections with descriptions and URLs via the ':returns:' note. It does not mention pagination or rate limits, but for a zero-argument list tool the behavioral profile is essentially complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences: the purpose is front-loaded, followed by usage guidance and the return contract, with no filler. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a trivial no-arg read tool with no output schema, the description covers purpose, when to use it, the sibling relationship, and the shape of the return value. Nothing an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so per the rubric the baseline is 4. The description doesn't need to explain parameter syntax, though it usefully notes that a returned section id is consumed elsewhere.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (list documentation sections) with a scope qualifier ('main sections of the deepset AI Platform documentation'). It is clearly distinct from the sibling search_docs, which it references as a complementary query tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use it ('to understand what documentation is available and help users navigate') and how it feeds into the alternative: passing a section id to search_docs as the section parameter. Both the use case and the hand-off to a sibling are spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and largely meets it: it discloses that each entry carries the newest version's id and declared Metrics (name, shape, grain, Label vocabulary) but not the source, and that output is auto-stored with an object ID usable with object store tools. It does not mention pagination limits or what an error message looks like, so it falls short of full disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded and the body is informative rather than padded. There is mild redundancy between 'can be referenced in other functions' and the closing 'Use the returned object ID to pass this result to other functions,' which repeats the same idea.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no parameters, the description must explain returns, and it does: it summarizes the returned fields and the object-ID handoff pattern. It stops short of describing pagination or error shapes, but is sufficient for a correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline of 4 applies; there is nothing for the description to disambiguate. Describing the shape of returned entries is useful but is not parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope ('Lists the Evaluators in the workspace') and then defines what an Evaluator actually is, distinguishing it from a generic list. An agent can tell this apart from the singular get_evaluator or from list_metric_groups without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: it explains that Evaluators belong to the workspace and that a Metric Group selects which ones a scoring run uses, which gives contextual orientation. But it never explicitly says when to choose this over get_evaluator or list_metric_groups, nor any when-not condition.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden, and it does substantial work: it discloses the status lifecycle (REPLAYING -> SCORING -> ENDED/FAILED per side), the semantics of the side's error and scoring_counts fields, and that output is auto-stored with an @obj_NNN preview. Gaps remain: no mention of pagination, result limits, or permissions for a list tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and filtering/routing content is front-loaded and useful, but the trailing object-store block repeats itself across four sentences ('The output is automatically stored...', 'Returns a formatted preview...', 'Use the object store tools...', 'Use the returned object ID...') and could be compressed to one. The domain paragraph is dense but earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description must cover returns and behavior; it states ':returns: The Experiments, or an error message' and explains the @obj_NNN preview mechanism. Combined with the status/error/scoring_counts lifecycle detail, an agent has enough to call and interpret it, though pagination/limits are unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the single parameter has no schema description, but the description compensates explicitly: 'pipeline_name: Only Experiments with either side on this pipeline, when given.' That clarifies both the matching semantics (either side) and the optional nature, adding real meaning beyond the bare anyOf string.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Lists the workspace's Experiments, newest first.' It then defines what an Experiment is and names the siblings it is not (get_experiment_results, get_experiment_run), so an agent can distinguish it from other experiment tools without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent: use get_experiment_results to compare candidate vs baseline, and get_experiment_run with scoring_runs[].experiment_run_id plus list_pipelines for cell-level reading. It also explains when the pipeline_name filter applies ('when given'). It lacks an explicit when-not-this-tool statement, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses key behaviors: read-only listing, pagination via cursor, automatic storage of output with object ID, and that the result is a formatted preview requiring further object store tools to explore nested properties.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is clear and front-loaded with the main purpose, but it includes some technical details about cursor and object ID that could be slightly more concise. Still efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple listing tool with one optional parameter and no output schema, the description provides all necessary information: purpose, pagination, output format, and how to use the result with other tools. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'after' is fully explained: it is a cursor for pagination, and its usage is tied to the response field 'next_cursor'. This compensates for the 0% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it retrieves a list of all indexes in the workspace, using a specific verb and resource. It distinguishes from siblings like create_index, get_index, and deploy_index by focusing on listing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied but not explicitly contrasted with alternatives like get_index for a single index. The description lacks guidance on when to choose list_indexes over other index-related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does a fair job: it discloses newest-first ordering, the null-vs-populated semantics of each entry's ``last_run``, that an error message may be returned instead of data, and the object-store side effect with ``@obj_...`` IDs. It stops short of permissions or rate-limit behavior, but the disclosed traits are concrete and non-obvious.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The tool-specific content is front-loaded and dense with signal, but the closing four sentences of object-store boilerplate repeat each other ('can be referenced in other functions' vs 'pass this result to other functions'). That template padding dilutes an otherwise tight description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no annotations exist, so the description must cover behavior and returns itself, and it largely does: entity definition, ordering, the ``pipeline_name`` effect on ``last_run``, error returns, and the object-store handoff are all present. The main gap is that a single-param tool is simple enough that this is close to sufficient without being exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 for the lone ``pipeline_name`` parameter, and it does: it explains that the parameter attaches each group's most recent scoring run on that pipeline, described without the grid (status, ``selection``, timing), and yields null when the group never ran there. That is meaning well beyond the bare ``anyOf[string, null]`` schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Lists the workspace's Metric Groups') plus an ordering ('newest first'), and then defines what a Metric Group is, which is what separates it from entities like evaluators or experiments in the sibling set. It never explicitly names a sibling to route away from, but the entity definition does the disambiguating work.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the effect of ``pipeline_name`` and routes the agent to ``get_experiment_run`` for reading a run's results, which is useful contextual guidance. It does not state when to prefer this tool over the other list_* siblings or any preconditions/exclusions, so usage remains implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the full burden. It explains pagination behavior (cursor, next_cursor) and automatic output storage with object IDs. However, it does not disclose idempotency, permissions, rate limits, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficient with about 6 sentences, front-loading the main purpose. Some sentences are explanatory but not overly verbose. Minor redundancy with object store instructions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 optional param, no output schema), the description adequately covers purpose, pagination, and output handling. Missing details on default behavior (e.g., first page without after) but overall sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description fully explains the 'after' parameter, its role in pagination, and how to interpret the response's next_cursor. This adds significant value beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves a list of all pipelines in the workspace, using specific verb 'Retrieves' and resource 'list of pipelines'. It distinguishes from siblings like 'get_pipeline' (single pipeline) and search variants.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing all pipelines with pagination support but does not explicitly state when to use this tool versus alternatives like search_pipeline. No exclusion or when-not-to-use guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses important behaviors: archiving delay of ~30 minutes, output stored and returned as object ID for use with object store tools, and pagination mechanics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with front-loaded purpose and detailed parameter sections. It is somewhat lengthy but each sentence adds value. Could be slightly more concise but overall effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description thoroughly covers input parameters, output structure (including fields in each entry), pagination, and how to use object IDs. It also mentions error handling briefly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% coverage, but the description provides extensive documentation for all 6 parameters including defaults, examples, supported fields for query_filter, and enum values for sort_field and sort_order.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves search history for a specific pipeline with pagination. It distinguishes itself from the sibling tool 'list_search_history' by focusing on pipeline-specific history.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the context for using this tool (getting search history for a pipeline) and provides detailed pagination guidance. However, it does not explicitly state when to use it versus alternatives like 'list_search_history'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full responsibility. It discloses what the summaries include (query_id, status, timing, failure details) and what they exclude ('do not include spans or logs'), and explains that the output is automatically stored and returns an object ID for later reference.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and return semantics, then parameter docs. It is longer than minimal, but the length is justified by the parameter richness and usage guidance; no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with six parameters, no output schema, and no annotations, the description fully covers return values, pagination, filtering, sorting, object ID handling, and differentiates from related tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description compensates fully with :param docs for all six parameters, including defaults, supported filter fields, enum values for sort_field, and OData filter examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.