Overseerr MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clear distinct purposes, but get_requests and get_user_requests both return request lists, and search_media and get_media_status both provide availability info. Descriptions explicitly call out the differences, avoiding major confusion.
Naming Consistency4/5
Tools mostly follow verb_noun snake_case (get_, search_, request_, update_, add_), but health_check breaks the pattern and singular/plural nouns vary (e.g., get_users vs get_issue). Overall consistent and readable.
Tool Count5/5
12 tools is well-scoped for an Overseerr server, covering users, media search, library search, request management, issue tracking, and health check without excessive overlap.
Completeness3/5
Core operations like search, request creation, status checking, and issue management are present, but the set lacks the ability to approve/deny requests—an essential Overseerr workflow. User management is also limited to listing, not editing roles or permissions.
Average 4.2/5 across 12 of 12 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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```
{
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  "maintainers": [
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states that the status is updated and that updated details are returned, but it does not disclose side effects, permissions, idempotency, or edge cases such as what happens if the issue is already resolved. As a mutation tool, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with Args/Returns sections. Every sentence contributes to understanding the tool, and there is no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and has an output schema, so the return note is not essential. However, the description lacks caveats like missing issue handling, status validation, or whether the update is a partial update. For an agent, this is minimally adequate but leaves some scenarios unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains both parameters: issue_id as 'The issue ID' and status as '"open" or "resolved"', which adds valid-value context beyond the raw schema. It is not overly detailed but sufficient for a two-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb and resource: 'Update an issue's status (open or resolve it).' It clearly distinguishes this from sibling tools like get_issues and add_issue_comment by indicating a write operation on status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives. The description implies usage by naming the action, but it does not mention prerequisites, exclusions, or alternative tools for related operations, unlike high-scoring examples that name specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It indicates this is a read operation ('Get') and discloses that the return includes all comments. However, it does not mention error behavior, authorization requirements, or whether any side effects occur. For a simple read tool, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with a clear summary, and uses structured 'Args' and 'Returns' sections. Every line adds necessary information without fluff, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with a single well-named parameter and an output schema to describe returns. The description covers the core purpose and return content adequately. It lacks elaboration on edge cases or permissions, but given its scope, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides zero description coverage (0%), so the description must compensate. The 'Args' section repeats 'issue_id: The issue ID' but adds no meaning beyond the parameter name itself. There is no discussion of format, source, or validation, providing minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get a single issue with its comments.' This specifies a unique resource and differentiates it from sibling tools like 'get_issues' (plural) and 'add_issue_comment'. The action verb 'Get' is direct and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: use this when you need a single issue by ID, as opposed to 'get_issues' for multiple. However, it does not explicitly name alternatives or state when not to use it. The context is clear but exclusions are not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It explains the return value (list of issues with type, status, reporter, media) and default parameter values, but does not mention potential side effects, authentication requirements, or rate limits. For a read-only listing tool, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening statement followed by a compact parameter list and return description. It is not overly verbose, though a slightly more concise phrasing could be achieved without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has only three optional parameters, all thoroughly explained, and an output schema exists. The description provides a complete picture of the tool's behavior and return values. The only missing context is differentiation from sibling tools like get_issue, which is more a usage guideline gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates fully by explaining each parameter: filter has allowed values ('all', 'open', 'resolved') and a default, limit has a default, and sort has allowed values ('added', 'modified') and a default. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get Overseerr issues (reports of problems with media).' The verb 'get' plus the specific resource 'issues' distinguishes it from sibling tools like get_issue (singular) and get_requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear explanation of the tool's filtering and sorting options, implying when it might be used (e.g., to list open issues). However, it does not explicitly state when to prefer this tool over alternatives like get_issue for a single issue, nor does it mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It states the action ('add') and returns the created comment, but doesn't discuss side effects, permissions, or failure modes. This is minimal but adequate for a simple write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a one-sentence purpose, a clear Args section, and a Returns section. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with an output schema, the description covers the essentials: what it does, what to pass, and what it returns. It lacks an example or mention of prerequisites (e.g., issue existence), but these are not critical for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description compensates by naming and explaining both parameters ('issue_id: The issue ID', 'message: The comment text'). This adds meaning beyond the schema, though it could be more detailed (e.g., format constraints).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add a comment to an issue') with a specific verb and resource. It distinguishes this tool from siblings like get_issues and update_issue_status, making its unique purpose obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use it (when adding a comment to an issue). It doesn't explicitly mention exclusions or alternatives, but the purpose is specific enough that no other sibling tool overlaps, making the intended usage unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It describes filtering behavior and returns, but does not explicitly state the read-only nature, auth requirements, or potential side effects. This is a moderate gap for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with an Args/Returns structure. Minor redundancy exists between the first two sentences, but overall it is well-organized and avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all parameters, provides a prerequisite, and summarizes the return value. Given the existence of an output schema, it does not need to detail return fields further. It lacks error handling or pagination notes, but these are not critical for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters are described with meaningful context in the Args section, despite the schema having no descriptions. It explains the meaning of user_id, the allowed values for media_status, and the semantics of limit and show_all. This fully compensates for the 0% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: retrieving all requests for a specific user. The name 'get_user_requests' aligns with this, and it differentiates from siblings like 'get_requests' by scoping to a user.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a direct prerequisite ('Get the user_id from the get_users tool'), indicating when to use this tool. It implies the appropriate context (specific user's requests) but does not explicitly contrast with alternative tools like 'get_requests'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It explicitly states this is a listing operation and discloses the return payload (IDs, names, request counts), which is sufficient for this simple read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat redundant, repeating 'all users' and the return fields multiple times. It is not poorly structured but could be tightened to a single sentence without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless list tool with an output schema, the description is adequate. It explains what the list contains and how to proceed with a user ID, covering the essential context despite no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds no parameter-specific details, but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get all Overseerr users' with a specific verb and resource. It distinguishes itself from siblings by focusing on listing all users, while tools like get_user_requests handle individual user requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides practical usage context by telling the user to use the returned user ID to get a specific user's requests, linking this tool to its sibling. It lacks explicit 'when not to use' language but implies the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It adds useful context that the tool verifies connectivity and returns server status/version, but it does not disclose potential error behavior, timeouts, or whether the operation is read-only. This is a gap, though acceptable for a simple health check.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, front-loaded with the core purpose. The structured 'Returns:' line adds useful information without redundancy. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simple nature, no parameters, and an output schema, the description adequately covers what the tool does and what it returns. It could mention failure behavior or connectivity error handling, but the presence of an output schema reduces the need for that detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the input schema is empty with 100% coverage (vacuously). The description correctly avoids inventing parameters. Baseline of 4 is appropriate for no-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Check') and resource ('Overseerr server status and connectivity'), clearly stating the tool's function. It distinguishes itself from sibling tools which all deal with users, media, requests, or issues, making this the only health-check tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the tool verifies MCP server connectivity to Overseerr, providing a clear use case. It does not name alternative tools, but no sibling serves the same purpose, so implicit guidance is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It transparently explains what the tool returns, including numeric status codes and fields like has_request and seasons_count, implying a read-only operation. It does not explicitly state 'does not modify anything,' but the language ('check', 'returns') strongly implies it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, starting with a clear purpose, followed by usage, then a bulleted return section. It is somewhat longer than necessary, but each part adds value, especially given the absence of annotations and schema descriptions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no annotations, the description covers purpose, usage, parameters, and return structure. It is missing details about error cases or behavior for invalid inputs, but given the existence of an output schema, the description is sufficiently complete for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has no descriptions (0% coverage), but the description compensates by explaining tmdb_id as 'TMDB ID of the movie or TV show (from search results)' and media_type as '"movie" or "tv"'. This adds meaningful context beyond the raw schema, including the valid values and source.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Check the availability status of a movie or TV show.' It uses a specific verb ('check') and resource ('availability status') and distinguishes itself from sibling tools like search_media and request_media by focusing on status retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage guidance: 'Use search_media first to find the TMDB ID, then check its detailed status.' This establishes a clear workflow and contextualizes when to use the tool. It doesn't explicitly mention alternative tools, but the prerequisite guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses filter behavior (status, media_status, days, limit, show_all) and return contents ('titles, requesters, request status, and media availability'), giving concrete expectations. It does not explicitly state read-only, but 'Get'/'Lists' and the return description make side effects absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded and structured into a purpose sentence, a usage example, and a clearly labeled Args/Returns block. Each line adds information; the example is the only non-essential addition but supports usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all five parameters, defaults, and return contents, making the tool usable without the schema or annotations. Minor ambiguities (e.g., exact interpretation of 'days' and default limit vs. 'all requests') keep it from being fully complete, but it is strong overall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description compensates by explaining every parameter: status values ('pending' or 'approved'), media_status values ('available', 'processing', 'unavailable', or 'failed'), days as last-N-days, limit max with default 20, and show_all ignoring limit. This adds meaning far beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Get media requests with user information' and adds 'Lists all requests showing who requested what and when,' which names a specific verb, resource, and scope. It clearly distinguishes from siblings like get_user_requests by emphasizing user information attached to all requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides an explicit use-case example ('Perfect for queries like list all requests from the last week and who requested them') that signals when this tool fits. It does not name alternatives or exclusions, so guidelines are clear but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that this tool submits a request, details the season auto-selection behavior, and discloses the return format. It lacks explicit mention of idempotency or permissions, but for this simple action it is adequately transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with an intro, workflow note, season clarification, parameter list, and return value. Every sentence earns its place, with no redundancy or filler. It is efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete for a tool of this complexity: it covers purpose, prerequisites, parameter usage, and return format. Even though an output schema exists, the additional return description is useful. The season handling logic is clearly explained, making the tool fully understandable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates by explaining each parameter: tmdb_id's source, media_type's allowed values, and seasons' format with examples. It also clarifies the 'all' option and the auto-selection for single-season shows, adding meaning far beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to request a movie or TV show be added to Plex. The specific verb 'Request' plus the resource 'movie or TV show' distinguishes it from sibling read-oriented tools like get_requests or search_media. The workflow hint to use search_media first reinforces its role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear prerequisite (use search_media first) and conditional guidance for TV shows with multiple seasons. While it does not explicitly list alternatives or when-not-to-use cases, the context of being the only request-creation tool among siblings makes the usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the substring matching behavior and gives an example ('dracula' matches 'Bram Stoker's Dracula'), which is crucial for correct use. It also mentions the return fields. It doesn't state read-only nature, but 'search' implies non-mutating.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with purpose, usage, alternative, args, and returns sections. It is slightly verbose but every sentence contributes useful information. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, and the description covers purpose, usage, parameters, and return values. An output schema exists, so the return details are redundant but helpful. It lacks mention of error handling or edge cases, but these are not critical for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero descriptions, but the description fully compensates by explaining both parameters: 'query' as title substring and 'media_type' as an optional filter restricted to 'movie' or 'tv'. This adds clear meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the user's Plex library for movies and TV shows they already have. It uses a specific verb and resource, and explicitly distinguishes itself from search_media for new content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage context ('when a user asks what's in their library or wants to find content they already own') and names the alternative tool (search_media) for new content. This directly guides selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well by disclosing that it searches TMDB rather than Plex, and that it returns TMDB IDs needed for requests. It also lists return fields including availability status, adding useful behavioral context beyond a simple search.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: purpose, key distinction, usage context, parameters, and return format. Every sentence serves a purpose with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and no annotations, the description sufficiently covers what the tool does, when to use it, and what it returns. The presence of an output schema reduces the need to detail return values, and the description aligns well with that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates with an Args section explaining 'query' as the search term and 'media_type' as an optional filter with allowable values 'movie' or 'tv'. This adds meaning beyond the raw schema, though it could be slightly more detailed about the query's matching behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'Search for movies and TV shows in Overseerr' with a clear scope (TMDB external database) and return value (TMDB IDs). Explicitly distinguishes itself from the sibling tool search_library, which is a strong differentiator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives direct usage context: 'Use this to find media by title before making a request.' It also provides an explicit alternative: 'To search content you already own, use search_library instead.' This clearly tells the agent when to choose this tool over a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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