Filter agent RL methods by supervision, critic and task setting; retrieve source links and BibTeX.
Scored across 8 tools
Each tool has a unique, clearly defined purpose with no functional overlap. Fetch vs search, get vs search, filter vs list facets are all distinct operations, making it easy to select the right tool.
All tools follow the same 'Agentic_RL_<verb>_<object>' pattern with descriptive verbs like 'fetch', 'search', 'list', and 'get'. The naming is uniform and predictable, aiding discoverability.
The 8 tools cover the main dataset, evidence, method, and task operations without being excessive or sparse. The count is well within the typical range for a focused research toolkit.
The toolkit covers the full workflow: dataset overview, evidence and task retrieval, search capabilities, method filtering, and facet/source listing. No obvious gaps in the provided functionality.
Inspect ShellOps and ShellOps-Pro task counts, train/test splits, task types, published schemas, source files, license and citation.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. 'Inspect' strongly implies a read-only, non-destructive operation, but the description does not explicitly state that there are no side effects, nor does it mention authentication or rate limits. It is clearer than a generic 'update' but still lacks explicit behavioral guarantees.
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 a single, focused sentence that front-loads the action and resource, then enumerates the specific data categories. No words are wasted, and the enumeration is necessary to convey the scope of the overview.
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?
Since there is no output schema, the description compensates by listing the categories of information the overview will provide. It does not specify the exact structure or formatting of the returned data, but for a read-only overview tool, the listed categories are sufficient for an agent to understand what to expect.
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 has zero parameters and the schema is an empty object, so there is no parameter information for the description to add. The baseline for no parameters is high, and the description correctly does not introduce any irrelevant parameter 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?
The description uses the specific verb 'Inspect' and identifies the exact resources (ShellOps and ShellOps-Pro) and the data elements covered (task counts, splits, types, schemas, source files, license, citation). It clearly distinguishes the tool as a dataset overview rather than a search, fetch, or listing operation.
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 explicitly state when to use this tool versus the sibling tools, nor does it mention any prerequisites or exclusions. It only describes what the tool does, leaving the agent to infer that it should be used for an overview rather than for searching or fetching specific items.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a complete original evidence block by the evidence_id returned from search_evidence, including section anchor, version, equations, table cells, links, and attribution.
| Name | Required | Description | Default |
|---|---|---|---|
| evidence_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It states what the fetch returns (section anchor, version, equations, table cells, links, attribution), implying a read-only retrieval. It does not mention error handling or what happens for invalid IDs, but for a simple fetch operation this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the purpose immediately and includes all necessary details without redundancy. Every clause adds information (fetch, ID source, content components), making it highly efficient.
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 with one parameter and no output schema, the description provides sufficient context: it explains what to pass and what to expect in return. It does not cover error scenarios or relationship to other tools beyond search_evidence, but these are not critical for a basic get-by-ID 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?
The schema provides an empty description for evidence_id, and schema description coverage is 0%. The description compensates by specifying that the evidence_id is 'returned from search_evidence,' giving the parameter origin and expected semantic meaning. This is essential for correct invocation but does not include format constraints or 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?
The description clearly states a specific verb ('Fetch') and a specific resource ('complete original evidence block'), and differentiates from search_evidence by specifying that it retrieves the full block using an ID. It also enumerates the returned content (section anchor, version, equations, etc.), adding concreteness beyond a generic fetch.
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 that the evidence_id is 'returned from search_evidence,' which gives clear guidance on the correct usage flow (search first, then fetch). However, it does not explicitly mention when not to use this tool or contrast with other sibling tools like dataset_overview or list_sources, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filter agent RL credit-assignment methods by research conditions and return original section evidence and BibTeX. Discover accepted values with list_method_facets. Filters combine with AND; empty strings leave a facet unrestricted. Unknown critic status never matches no. Results use publication order without a relevance or quality ranking.
| Name | Required | Description | Default |
|---|---|---|---|
| credit_granularity | No | ||
| evaluation_setting | No | ||
| learned_value_critic | No | ||
| required_supervision | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden and discloses important matching behavior: AND combination, empty-string handling, unknown critic status never matching 'no', and publication-order results. It does not explicitly state that the operation is read-only, but the filtering language and return of evidence strongly imply a non-destructive query.
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 compact and information-dense, packing the action, return type, matching semantics, facet-value discovery, and ordering behavior into a few clear sentences. There is no filler or redundant phrasing.
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 an output schema, the description adequately states that the result includes original section evidence and BibTeX, and it clarifies ordering and filter semantics. It does not mention pagination or output structure, but for a filtering tool the provided context is sufficient for most agent use cases.
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 provides no per-parameter descriptions, and the tool description only names the facets without explaining each one's meaning or accepted values. The instruction to discover accepted values via list_method_facets helps, but it does not compensate for the lack of detail on what credit_granularity, evaluation_setting, learned_value_critic, and required_supervision represent or how they are formatted.
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 a specific action: filtering agent RL credit-assignment methods by research conditions, and identifies the returned artifacts as original section evidence and BibTeX. It also distinguishes the tool by pointing to list_method_facets for accepted values, making its role distinct from the sibling search and overview 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 gives concrete usage semantics: filters combine with AND, empty strings leave facets unrestricted, and results are in publication order without ranking. It does not explicitly contrast this tool with sibling search tools, but the facet-filtering behavior and the pointer to list_method_facets provide enough guidance for correct use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Inspect one published ShellOps or ShellOps-Pro task by its exact task_id and partition ('shellops' or 'shellops_pro'). Returns the complete instruction, actual reward specification, published reference answer/command, file-entry metadata, pinned parquet rows and workspace asset links. File content is available at the source links. No shell execution or solution verification is performed.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | ||
| partition | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the return content (instruction, reward spec, reference answer, metadata, parquet rows, asset links) and explicitly notes that file content is only available at source links and that no execution or verification occurs. This is transparent about what the tool does and does not do, though it could mention potential error cases or 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 description is two sentences with no waste, front-loading the primary purpose and then detailing the return value and exclusions. Every sentence earns its place, and the structure is efficient.
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 read-only inspect tool with no output schema and no annotations, the description is quite complete: it lists all returned data categories, states file content is at links, and clarifies non-actions. Minor gaps like pagination or error handling exist but are not critical for this tool's straightforward purpose.
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 zero description coverage for both parameters, so the description fully compensates by defining task_id as 'exact task_id' and partition as restricted to 'shellops' or 'shellops_pro'. This adds essential meaning beyond the empty schema fields, making parameter usage clear.
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 uses a specific verb ('Inspect') with a clear resource ('published ShellOps or ShellOps-Pro task') and identifies the exact identifiers required (task_id and partition). It distinguishes itself from sibling tools like search_tasks by emphasizing the need for an exact ID, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: 'by its exact task_id and partition' signals that this tool is for retrieving a known task, not for searching. It also clarifies what it does not do ('No shell execution or solution verification'), helping the agent decide when to use it. However, it does not explicitly name alternative tools, though 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.
List exact filter values for agent RL credit granularity, supervision, value critics and evaluation settings. Each value reports its source-supported method count.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only says 'List', implying a read-only operation, but does not explicitly state side effects, permissions, rate limits, or other 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 concise, consisting of two short sentences: the first states the core purpose, the second adds a relevant output detail (method count). No unnecessary words or redundant 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?
Given the simplicity of the tool (no parameters, no output schema), the description is complete. It specifies exactly what is listed and that each value reports a method count, which is sufficient for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema_description_coverage is 100%. According to the rubric, a baseline of 3 is appropriate. The description does not need to add parameter information since there are none.
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 verb 'List' and the specific resource 'exact filter values for agent RL credit granularity, supervision, value critics and evaluation settings'. It also differentiates from sibling tools like list_sources and filter_methods by focusing on filter values, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives such as search_evidence or filter_methods. It implies its use for obtaining filter values before filtering, but lacks explicit guidance or mention of 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.
List original papers and retrieval coverage. Discover source-linked comparisons of credit assignment, agent memory, selective observation and terminal benchmarks, with JSON, CSV and BibTeX links.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool lists papers and coverage and that JSON, CSV, and BibTeX links are part of the output, which is useful. However, it does not state whether results are read-only, paginated, ordered, or scoped, though the lack of parameters reduces the risk.
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 two sentences and front-loads the core action ('List original papers and retrieval coverage'). The second sentence adds concrete topic scope and output formats without padding or redundant schema repetition.
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 parameterless list tool with no output schema, the description is mostly complete: it states what is listed, the subject areas, and the available link formats. The term 'retrieval coverage' is somewhat ambiguous, but an agent can still invoke the tool correctly without additional context.
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 has zero parameters and the schema description coverage is 100%, so the baseline is 4. The description does not need to explain parameter meaning, and it appropriately focuses on output and content rather than inputs.
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 names a specific verb and resource ('List original papers and retrieval coverage') and adds topic context ('credit assignment, agent memory, selective observation and terminal benchmarks'). It does not explicitly distinguish itself from siblings like Agentic_RL_search_evidence or Agentic_RL_fetch_evidence, but the listing intent is reasonably clear.
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 guidance is given on when to use this tool versus the sibling search/fetch tools. The description implies a browsing/discovery use case, but it never states exclusions or alternatives, leaving an agent to infer the appropriate selection context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search original papers on agentic reinforcement learning, credit assignment and CLI agents. Use English keywords (AND), OR and quoted phrases. Return relevant passages, source citations, equations and table cells.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns passages, citations, equations, and table cells, which implies a retrieval operation. However, it does not explicitly state side effects (e.g., read-only), limitations, or error behavior.
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 within two sentences. It directly states the purpose, gives query syntax, and lists return content without unnecessary 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?
The description covers the core function, query syntax, and return content, but lacks details about output format, result structure, or edge cases. Given the absence of an output schema and parameter descriptions, more context would be helpful for an agent to fully understand expected 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?
Both parameters have empty descriptions in the schema, so the description must compensate. It indirectly explains 'query' by describing keyword syntax, but it does not explicitly define the parameter or the 'limit' parameter beyond its default value. This leaves significant ambiguity.
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 ('Search original papers') and the subject domain ('agentic reinforcement learning, credit reinforcement learning and CLI agents'). It does not explicitly distinguish itself from the sibling tool 'search_tasks', which could cause ambiguity, but the focus on 'original papers' provides some differentiation.
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 explicit usage instructions for query syntax ('Use English keywords (AND), OR and quoted phrases') and describes the expected return content. It does not clarify when to use this tool versus alternatives like 'search_tasks', but the operational guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find real ShellOps CLI benchmark tasks by case-insensitive literal substring in the complete instruction, task ID or published task type. Empty query lists all tasks. Select partition 'all', 'shellops' or 'shellops_pro'; select published split 'all', 'train_src', 'train' or 'test'. Results are ordered by partition then task ID, with explicit pagination and no relevance scoring. The train subset is not double-counted.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| split | No | all | |
| offset | No | ||
| partition | No | all |
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 thoroughly: it discloses case-insensitive literal substring matching, the fields searched, empty-query behavior, ordering by partition then task ID, explicit pagination, no relevance scoring, and the nuance that the train subset is not double-counted. These are non-obvious behavioral traits that help the agent anticipate results.
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 tight and front-loaded: the first sentence states the core purpose, followed by clarifying behaviors. Each sentence adds unique value—empty-query behavior, allowed values, ordering/pagination, and the train double-count caveat. No redundancy or 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?
Given a search tool with five parameters and no output schema, the description covers the essential operational details: what is searched, how to filter, ordering, pagination, and an edge case about split counts. It implicitly indicates the return type (tasks) and is sufficient for an agent to invoke 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%, so the description must compensate. It explains the meaning of query (substring), partition ('all', 'shellops', 'shellops_pro'), and split ('all', 'train_src', 'train', 'test'), and implies limit/offset via 'explicit pagination'. It does not explicitly define limit/offset constraints, but given conventional naming, the added meaning is substantial beyond 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?
The description begins with a specific verb ('Find') and a clear resource ('real ShellOps CLI benchmark tasks'), then defines the search scope (substring in complete instruction, task ID, or published task type). This distinguishes it from siblings like Agentic_RL_search_evidence (searches evidence) and Agentic_RL_get_task (retrieves a specific task).
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 clear context on how to filter results via partition and split, and explains ordering and pagination behavior. However, it does not explicitly state when to use this tool versus siblings (e.g., 'use get_task when you have a task ID'), though the verb and resource imply the use case. There are no exclusions or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Agentic_RL_filter_methodsAgentic_RL_list_method_facetsAgentic_RL_dataset_overviewAgentic_RL_fetch_evidenceAgentic_RL_get_taskAgentic_RL_list_sourcesAgentic_RL_search_evidenceAgentic_RL_search_tasksReviews of arXiv papers for AI agents: verdicts, claims, flaws, compiled records, citation graphs.
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