luxalgo-mcp-server

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

With no annotations provided, the description carries the transparency burden. It discloses the return format (markdown), the content structure (definition, formula, interpretation), and a key requirement (exact slug). While it doesn't mention error behavior or rate limits, for a simple read tool this is sufficient.

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?

Two sentences with no filler. The first sentence states what the tool does and what the output includes; the second gives usage context and input prerequisites. Perfectly sized and front-loaded.

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 single-parameter read tool with no output schema, the description covers the purpose, input requirements, output format, and usage context. It is fully sufficient for an agent to select and invoke 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 schema already fully describes the slug parameter (100% coverage). The description adds essential advice about using the exact slug and how to find it via search or list tools, which is extra semantic 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's action ('Explain a trading concept') and specifies the output format ('markdown — definition, formula, how traders read it, and its indicator implementations'). It also implicitly distinguishes itself from siblings like library_get_indicator and library_get_family by focusing on concepts.

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 for any "what is X / how does X work" question.' Also provides a prerequisite and solution: 'Needs the exact slug — find it with library_search or library_list_concepts.' This gives clear context and actionable 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 burden. It discloses the output format (markdown) and content (overview + roster), which is transparent for a read-only get operation. However, it does not explicitly state that it has no side effects, though 'get' implies this.

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?

Two sentences, no wasted words, front-loaded with the tool's purpose. All information is relevant and necessary.

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 with no output schema, the description fully covers what it does, when to use it, and what to expect in the response. No gaps in 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?

Schema coverage is 100% with an enum and description. The description adds user-friendly examples ('SMC', 'Wyckoff') and explains the concept of a 'whole area', which enriches understanding beyond the schema alone.

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 returns a family hub page as markdown, including an overview and complete concept roster. It distinguishes from siblings by specifying this is the hub page for a family, contrasting with library_get_concept or library_list_families.

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 provides when to use: after library_list_families, or when the user asks about a whole area like 'SMC' or 'Wyckoff'. This gives direct context and differentiates from alternative 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?

The description discloses what details are returned (family, concept links, preview image, source code availability) and clarifies that fetching code is done via a separate tool. With no annotations, it carries the transparency burden effectively, though it doesn't mention authentication or rate limits, which are likely unnecessary for a read-only getter.

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?

Two sentences pack all essential information in a front-loaded manner: first sentence lists what the tool returns, second sentence gives usage. 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?

For a single-parameter read tool with no output schema, the description is comprehensive: it covers the content returned, when to use it, and points to a sibling for code retrieval. It fully equips an agent to decide and invoke 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 one parameter (slug) with a clear example ('tri-star'), achieving 100% schema coverage. The description does not add parameter-specific semantics, so the baseline of 3 applies.

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 'Details for one indicator' and enumerates the content types (what it does, how to read it, family, concept links, preview image). It explicitly distinguishes itself from library_get_source_code, making it distinct 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?

Provides explicit usage guidance: 'Use when the user asks about a specific indicator.' It also directs the agent to library_get_source_code for fetching code, offering a clear alternative and preventing misuse.

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 transparency burden. It adds meaningful context: the code is 'full' and 'working', and it mentions the rationale for separation (long sources). While it doesn't describe return format or side effects, for a read-only retrieval tool this is sufficient.

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, front-loaded with the primary purpose, and efficiently explains when to use it. 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.

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

Given a single parameter and no output schema, the description covers the essential purpose and usage. One minor gap is not stating the return format (e.g., the code as a string), but the description's statement about 'full, working source code' implies the output without 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 already describes the single 'slug' parameter at 100% coverage, so the baseline applies. The description does not add extra semantic detail about the slug format or where to find it, but the schema's description is adequate.

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 title and description clearly state that this tool retrieves the full, working source code of a Library indicator. It explicitly distinguishes itself from library_get_indicator by noting that it is kept separate because sources are long, making the purpose concrete and identifiable among 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 explicit guidance: 'call it only when the user wants the code itself.' It also contrasts with library_get_indicator, implying that library_get_indicator is for other indicator details, giving clear when-to-use and when-not-to-use context.

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?

Despite no annotations, the description discloses key behaviors: pagination, optional family filtering, and full enumeration ('every'). It does not explicitly declare read-only nature, but 'Browse' strongly implies it, and listing is inherently non-destructive.

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?

One concise sentence conveys purpose, behavior, and alternatives without redundancy. Front-loaded with the core action and resource.

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 no output schema, the description adequately conveys scope and usage. It hints at return contents (slugs for library_get_concept) and pagination, though it could mention the structure of each concept entry.

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 covers 67% of parameters with defaults for page and page_size, while family has an enum but no description. The description adds context about pagination and optional family filtering, but does not further clarify parameter meanings 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 uses a specific verb 'Browse' and identifies a clear resource: 'every trading and technical-analysis concept in the Library'. It also distinguishes itself from siblings by mentioning the alternative library_search for keyword lookup.

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 to enumerate a topic area or find slugs for library_get_concept', and directs users to 'prefer library_search' for keyword lookup, creating clear decision boundaries.

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 what the call returns: 17 families, counts, and hub links. It implies a read-only listing behavior and provides concrete output expectations, which is sufficient for this simple 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?

Two sentences, front-loaded with the core purpose and immediately followed by specific output details. No wasted words.

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 zero-parameter listing tool with no output schema, the description fully covers what the agent needs: what it does, what it returns, and how it fits into the overall library exploration 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 tool has zero parameters, so the description carries no parameter burden. The schema is empty, and the baseline for 0 params is 4; the description appropriately focuses on the output, not 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 description clearly states the tool lists the Library's top-level taxonomy with 17 families and includes concept counts and hub links. It distinguishes itself from siblings by emphasizing its top-level scope and first-call role.

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 positions this as 'the natural first call for orientation', giving clear context for when to use it. It doesn't name alternatives or when-not-to-use, but the sibling tools list provides the landscape.

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 reveals that sorting is server-side and defaults to newest first, which is valuable. However, it does not mention pagination behavior, potential rate limits, or that the operation is read-only, leaving some behavioral ambiguity.

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, front-loaded with the primary action and resource, and every clause adds useful information without waste. It is compact and well-structured.

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 list tool with six parameters, no output schema, and no annotations, the description gives enough context to select and invoke it: purpose, use cases, and behavioral sort defaults. It does not explicitly describe the return format or pagination, but the schema and the general browsing context make this acceptable.

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 covers 67% of parameters with descriptions, and the description adds semantic value beyond the schema: it clarifies the default sort direction ('newest first') and gives practical filtering examples. This helps infer the meaning of family and direction parameters even though they lack explicit 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 clearly states the tool browses the indicator catalog with filters and server-side sorting, using a specific verb and resource. It distinguishes itself from the sibling library_search by explicitly mentioning keyword discovery as a separate use case.

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 gives explicit when-to-use guidance: 'Use for structured browsing' with concrete examples ('latest indicators', 'everything in the volatility family'), and explicitly directs keyword discovery to library_search. This provides clear alternatives and avoids ambiguity.

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 alias-aware behavior ('stochastics' finds Stochastic Oscillator), the breadth of the search (800+ concepts and indicators), and the output characteristics (slugs for get tools, canonical URLs). This is useful behavioral context, though it does not cover potential details like result ordering or rate limits, which are not critical here.

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 concise sentences, front-loaded with the tool's primary action ('Search the LuxAlgo Library') and followed by essential usage and output notes. Every phrase contributes information: scope, alias-awareness, usage guidance, and result contents. There is no filler or redundancy.

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 adequately explains what the agent can expect: 'results carry slugs for the get tools plus canonical URLs for citation.' It also covers what the search covers (concepts and indicators) and provides a clear starting point. Given the tool's relative simplicity and the rich sibling context, the description is complete enough 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?

The input schema already has 100% coverage with descriptive text for all parameters, so the baseline is 3. The description adds meaningful value beyond the schema by explaining query alias-awareness with an example, which directly enriches the 'query' parameter semantics. No other parameters are elaborated in the description, but schema already handles them well.

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 'Search the LuxAlgo Library', a specific verb+resource, and immediately clarifies the scope: 'over 800+ concepts and 800+ ready-to-use indicators.' It distinguishes from siblings by stating 'Start here whenever you have a name, informal term, or topic' and noting results carry slugs for the get tools, which clearly separates this search tool from listing and retrieval 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 explicitly says 'Start here whenever you have a name, informal term, or topic', providing a clear directive for when to use this tool. However, it does not explicitly state when NOT to use it (e.g., when you already have a slug) or mention alternatives beyond implied slug-based get tools, 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.