Tripitaka MCP (hosted)

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

The description discloses important behavioral traits: the current state (translation table empty, only default Pali+English loaded, total_editions often 0, text_pali and text_english always available). This goes beyond any missing annotations and helps set expectations.

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

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

The description is well-structured with emojis and clear sections (when to use, vs get_sutta, format, current state). It is front-loaded with the core purpose. While slightly long, every sentence adds value and earns its place.

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

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

Given that an output schema exists (true), the description appropriately focuses on usage context and limitations. It explains the tool's scope, parameter format, and current data state, providing sufficient completeness for an AI agent to invoke it correctly.

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

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

The input schema already describes segment_id with an example. The description adds value by explaining the format (<sutta_id>:<paragraph>.<line>) and noting that segment_id can be obtained from get_sutta or search results. This enhances comprehension beyond the schema.

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

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

The description clearly states the tool's purpose: 'เปรียบเทียบคำแปลทุกฉบับที่มีสำหรับ segment เดียวกัน' (compare all translations for a single segment). It specifies the resource (segment) and distinguishes itself from the sibling tool get_sutta by targeting line-level rather than whole sutta.

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 when-to-use scenarios (e.g., user asks meaning of a single line, checking translator differences, academic work) and contrasts with get_sutta. It also explains the format of the segment_id parameter and where to find it.

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

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

No annotations provided, but description discloses that the tool returns only metadata and citation, implying read-only behavior. Does not mention potential side effects, but for a citation generator 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?

Description is concise, well-structured with bullet points and emojis, no redundant sentences. Front-loads the primary purpose.

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

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

Tool has output schema and simple input (one parameter). Description covers purpose, usage, and sibling comparison. Lacks error handling or prerequisite info but is generally complete for this tool's complexity.

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 a clear description of sutta_id including examples. Description does not add extra parameter meaning beyond the schema, so baseline of 3 is appropriate.

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

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

States clearly that the tool creates correct citations for suttas. Distinguishes from sibling get_sutta by specifying it returns only metadata and citation, not segments.

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 lists when to use (citation requests, need position in Tripitaka, need formatted citation) and contrasts with get_sutta for combined content+citation needs.

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 explains that the tool returns full segment content, notes the presence of cross_reference fields for verification, and discloses limitations (e.g., Thai translation currently unavailable). It could be more explicit about idempotency and error handling, but overall provides substantial behavioral context.

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

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

The description is lengthy and includes a coverage section that may be redundant for a tool description. While well-structured with clear sections, it could be more concise by trimming examples and moving some contextual info to documentation.

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 complexity (3 params, output schema, diverse usage scenarios), the description covers most aspects: input format, output expectations, behavioral notes, and coverage. It lacks mention of error handling or behavior for invalid IDs, but is otherwise thorough.

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%, so baseline is 3. The description adds value by providing extensive examples for sutta_id, clarifying the format and valid values, and noting current limitations for edition and language parameters (e.g., Thai translation not available). This goes beyond the 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's function: retrieving full sutta content by standard ID. It provides numerous examples and distinguishes itself from search tools by focusing on direct ID-based retrieval.

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

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

The description includes a dedicated 'คำแนะนำสำหรับ AI client' section with practical tips on interpreting segments, quoting directly, and handling special cases like headers and colophons. However, it lacks explicit guidance on when to use this tool vs. sibling tools like search_by_keyword.

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 burden of disclosure. It explains that the tool returns definitions and sutta context, and notes that the source is original academic work always accessible. It also alerts that results may be in Thai and need translation. No destructive behavior is mentioned, which is appropriate for a read tool. Minor omission: no mention of error handling or missing words.

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 starts with a clear one-sentence purpose statement, followed by a usage line and an explanatory paragraph about the dictionary database. The extra paragraph is useful but slightly wordy. The use of emojis adds visual structure but is not essential. Overall it is well-structured and informative, though could be trimmed.

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 that an output schema exists (not shown), the description does not need to detail return values. It covers the main purpose, data source, and behavior regarding Thai content. It lacks information about error handling or word availability, but for a dictionary lookup tool, this is reasonably complete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add significant value beyond the schema; for example, it doesn't enumerate available languages or clarify how limit_context affects output. The parameter descriptions in the schema are already 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 clearly states the tool retrieves dictionary definitions of Pali words along with contextual example sentences. It provides a specific verb ('ดึงความหมาย' – retrieve meaning) and resource ('พจนานุกรมของคำศัพท์บาลี'), and distinguishes it from sibling tools like search_by_keyword by focusing on definitions with context.

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 positions the tool as a 'Pali Dictionary Bridge' for understanding true meaning, implying it is for definitions rather than full-text search. It notes that the dictionary content is always available even if Thai is disabled, guiding multilingual use. However, it does not explicitly contrast with sibling tools or state when not to use it.

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

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

With no annotations, the description fully discloses behavior: filtering by enabled languages, current indexing state for Thai editions, and the exact structure of returned objects. No contradictions.

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

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

Well-structured with emojis and sections, but slightly verbose. Front-loads key information, though could be more concise without losing clarity.

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?

Complete coverage for a no-parameter tool with described output schema. Includes filtering logic, state caveats, and return structure, leaving no significant gaps.

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

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

No parameters exist, so baseline is 4. Description adds meaning by explaining output fields and behavior, but no parameter semantics are needed.

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

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

Description clearly states the tool lists editions with coverage statistics, with specific verb 'แสดงรายการ' (list) and resource 'ฉบับแปล' (editions). It distinguishes itself by providing usage context relative to sibling tools like compare_translations and get_sutta.

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 (before compare_translations or get_sutta, when user asks about editions) and provides filtering behavior based on server configuration. Also notes current state limitations, giving clear guidance on expected results.

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?

Since no annotations are provided, the description fully bears the burden of behavioral disclosure. It details important quirks such as duplicate legacy and modern codes, instructs to use codes with segment_count > 0, notes language support (Pāli, Thai, English labels) and the absence of Thai translations. This is comprehensive.

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 somewhat lengthy but well-structured with bullet points, emoji markers, and clear sections. Every sentence adds value. Minor improvement could be trimming redundant phrases.

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 zero parameters and an explicit output schema described in the 'Returns' section, the description is complete. It explains the hierarchical structure, language handling, known quirks, and current data state, leaving no ambiguity for the agent.

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 no parameters, so the description cannot add parameter-specific meaning. With 0 parameters, the baseline is 4. The description focuses on output and behavior instead, which is appropriate.

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

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

The description clearly states the tool's purpose: showing the hierarchical structure of the Tripitaka with coverage statistics. It specifies the three pitakas and what each contains, making it distinct from sibling tools like get_sutta or search_by_keyword.

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 lists three scenarios when to use this tool: for overview questions about the Tripitaka, checking coverage before promising search capabilities, and verifying scope for artifact compilation. This provides clear context and implicitly suggests when not to use it.

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

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

No annotations provided, so description must cover behavior. It explains the rule-based nature, limitations (no sandhi, no compound splitting), and that results are possible stems. Could mention idempotency or error handling, but adequate for a parse 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?

Well-structured with emoji headings, bullet points, and workflow. Informative but not overly verbose. Every sentence adds value. Slightly long but justified by complexity.

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

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

Given the tool's complexity (Pali grammar), the description is thorough: covers purpose, when to use, workflow, limitations. Output schema exists, so return values need not be detailed. Contextually complete.

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

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

Schema coverage is 100% with a clear description for the 'word' parameter. The description adds examples but doesn't provide additional semantic constraints or format details beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: analyzing Pali words for stemming/lemmatization. It provides examples (dukkhassa, bhikkhūnaṁ) and distinguishes from sibling tool get_word_definition by specifying when to use which.

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?

Explicit guidance on when to use: when get_word_definition fails, for compound words, and to see possible stems. Recommends a workflow: parse then call get_word_definition. Also lists limitations, 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?

Since no annotations are provided, the description carries full burden. It explains trigram matching, language dependency, filtering options, and error behavior for disabled languages. It does not cover pagination but mentions limit defaults and max.

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 long but well-structured with sections and bullet points. It front-loads purpose and then gives usage advice. Each sentence adds value, though it could be slightly more concise.

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

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

Given the tool has 5 parameters and an output schema, the description covers all critical aspects: when to use, how to use, comparisons to siblings, behavioral traits, and parameter semantics. It is comprehensive.

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

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

Schema coverage is 100%, baseline 3. The description adds significant meaning: keyword translation advice, pitaka scope with reference to list_structure, edition conditionality on language, and language default/error handling.

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 searches the Tripitaka by keyword using trigram similarity. It distinguishes itself from sibling tools like search_hybrid, explicitly noting this is for term lookup, not concept search.

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

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

Provides explicit guidance: use for term lookup, use search_hybrid for concept search. Also advises on language translation to Pali Roman or English if the user's language is not supported.

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

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

No annotations are provided, so the description carries the full burden. It explains the RRF fusion technique, ranking behavior, and limitations like language effectiveness. It does not explicitly state non-destructive nature, but as a search tool this is implicit.

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

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

The description is well-structured with sections and bullet points, front-loading the purpose. It is somewhat verbose but each sentence adds value, though could be tightened.

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 complexity of hybrid search and existence of output schema, the description covers usage tips, limitations, and technical details thoroughly, enabling effective tool usage.

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%, and the description adds significant value: for query it explains language effectiveness, for limit it recommends higher values for surveys, and for language it clarifies options.

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

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

The description clearly states it is a hybrid search combining keyword and semantic search using RRF. It identifies the use case ('recommended for finding content about topic X') but does not explicitly differentiate from sibling tools like search_by_keyword and search_semantic, though the combination is implied.

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 specific recommendations for AI clients: using English queries, handling stop words, adjusting limit for coverage, and understanding ranking is similarity-based not canonical. It lacks explicit when-not-to-use guidance.

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

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

No annotations exist, so the description carries full burden. Discloses use of cosine distance, multilingual MiniLM model, language-specific performance (English best, Thai weakest), and known limitations like index being only Pāli and dispersion of scores. Lacks explicit mention of read-only nature, but context implies no side effects.

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

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

Description is well-structured with a concise first line, bullet points for usage and limitations. Every sentence adds value, though slightly verbose. Good front-loading of core purpose.

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

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

Given the tool complexity, 100% schema coverage, and existence of output schema, the description comprehensively covers purpose, usage guidelines, parameter semantics, and limitations. No significant gaps.

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

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

Schema coverage is 100%, baseline 3. Description adds meaningful context: explains query language effectiveness, threshold meaning as cosine distance with tuning guidance, and default/max values. Exceeds baseline by providing practical usage tips.

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 performs semantic vector similarity search on text_pali, distinguishing it from keyword search. It explicitly contrasts with sibling tools like search_hybrid and search_by_keyword.

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 advises using search_hybrid for most cases and provides specific scenarios for this tool: pure semantic search, fine-grained threshold tuning, and debugging. Also recommends search_by_keyword for exact Pāli terms.

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