New Mexico Literacy Project
Server Details
Free book-donation pickup logistics and first-edition identification tools for New Mexico.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool access control
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Usage analytics
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Tool Definition Quality
Average 4/5 across 12 of 12 tools scored. Lowest: 3.4/5.
Each tool has a clearly distinct purpose, covering separate aspects like coverage checking, scheduling, first edition identification, and reference lookups. No two tools overlap in functionality.
All tools follow a consistent 'nmlp_verb_noun' pattern using snake_case. Verbs like get, check, decode, identify, lookup, schedule, and search are predictably paired with specific nouns.
With 12 tools, the surface is well-scoped for the domain: donation logistics, book identification, and reference materials. Each tool earns its place without feeling excessive or sparse.
The tool set covers core workflows (coverage check, scheduling, first edition identification, and knowledge retrieval). Minor gaps like pickup status tracking or archive updates are absent but not critical.
Available Tools
12 toolsnmlp_check_coverageAInspect
Check whether NMLP picks up books at a given five-digit US ZIP code. Returns coverage tier (core_metro, metro, near_metro, statewide_large_only, out_of_area), typical pickup window in days, minimum quantity, and a human-readable message. ALWAYS call this BEFORE nmlp_schedule_pickup.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | Yes | Five-digit US ZIP code |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully discloses that it checks coverage (read-only implied) and returns specific data. It does not explicitly state side effects, but none are expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero wasted words. First sentence covers purpose and outputs; second gives essential usage rule.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description is complete. It covers purpose, output fields, and a critical usage ordering instruction.
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% for the single parameter 'zip'. Description adds context by explaining that the ZIP is used to check coverage, which is beyond the schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'check' and resource 'coverage at a given ZIP code'. It lists specific output fields and distinguishes from sibling nmlp_schedule_pickup by indicating it should be called before.
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 says 'ALWAYS call this BEFORE nmlp_schedule_pickup', providing clear when-to-use and 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.
nmlp_decode_number_lineAInspect
Decode a copyright-page number line / printer's key (and any 'First Edition' wording) to determine which printing a book is. Paste the row of small numbers and/or the edition statement. Handles the Random-House-ends-in-2 exception and flags book-club editions. Returns the printing, a plain-English verdict, and the detected line.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Copyright-page text — the number line (e.g. '10 9 8 7 6 5 4 3 2 1') and/or 'First Edition' wording. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it handles exceptions, flags book-club editions, and returns specific data. It is a read-only decoding operation, so no destructive actions or authorization needs. It does not detail error handling or edge cases, but the core behavior is clear.
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: four sentences covering purpose, input, special behavior, and output. There is no redundancy or unnecessary detail. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter set and no output schema, the description is complete. It explains the input format, special cases, and return values. The tool's behavior is fully described for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single required parameter 'text'. The tool description adds significant meaning beyond the schema by specifying that the input should be the copyright-page number line and/or 'First Edition' wording, and that it can be pasted. This helps the agent understand the expected format and content.
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: decoding a copyright-page number line to determine printing. It specifies the input (number line and/or 'First Edition' wording), mentions handling of exceptions (Random-House-ends-in-2) and book-club flagging, and lists the output (printing, verdict, line). This distinguishes it from siblings like nmlp_identify_first_edition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use: to determine printing from a copyright-page number line. It describes what to input (the row of numbers and/or edition statement) and mentions special handling. However, it does not explicitly differentiate from the sibling tool nmlp_identify_first_edition 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.
nmlp_get_archiveAInspect
Get NMLP's donation archive entries as structured Book records.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the full burden of behavioral disclosure. It only states the return type as 'structured Book records' but does not mention whether the operation is read-only, requires authentication, has rate limits, or any other behavioral traits.
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 sentence of 9 words, precisely stating the action and output. It is front-loaded with the core purpose and contains no superfluous 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 (zero parameters, no output schema), the description adequately covers the return type and action. It could be improved by clarifying that the operation is read-only or mentioning if any context is needed, but overall it is sufficiently complete for an agent to understand its use.
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 schema coverage is 100%. According to guidelines, no parameter details are needed, earning a baseline of 4.
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 'get' and the specific resource 'NMLP's donation archive entries', and indicates the output format as 'structured Book records'. It effectively distinguishes this tool from siblings like nmlp_get_donation_options and nmlp_search_titles.
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 provided on when to use this tool versus its siblings. The description only states what the tool does without any conditions, prerequisites, or scenarios where it is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_get_business_cardAInspect
Get NMLP's canonical business entity card — address, phone, services, area served, languages.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only mentions what data is returned. It does not disclose behavior like side effects, authentication needs, or rate limits. The 'Get' verb implies read-only, but no explicit safety guarantee.
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?
Single sentence that is direct and without unnecessary words. Front-loaded with the key 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?
With no output schema and no parameters, the description adequately summarizes what the tool returns. It lists all expected fields, but could mention consistency or completeness of the data.
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 schema coverage is 100%. Description adds value by listing response contents (address, phone, services, area served, languages). Baseline for 0 params is 4.
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 retrieves the business entity card and lists its contents (address, phone, services, area served, languages). It is specific and distinguishes from sibling tools that focus on other aspects like archives or donations.
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 on when to use this tool versus alternatives. Given many sibling tools, explicit comparison would help, but it only describes what the tool does.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_get_donation_optionsAInspect
Get the comparison matrix of every Albuquerque book donation option (NMLP, Goodwill, Savers, Better World Books, Friends of APL, Habitat ReStore, regional pulper).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the tool is a read operation ('Get'), but with no annotations provided, it does not address potential side effects, permissions, or data source details. For a zero-parameter get tool, this is acceptable but could be improved by noting if it requires any external service or has caching 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 a single sentence with no unnecessary words. It front-loads the action and resource, and every element provides value. This is ideal for quick parsing by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and a straightforward purpose, the description is complete. It explains what the tool does, what it returns, and the specific entities it covers. No additional context is needed for effective invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the input schema is fully covered. The description does not need to add parameter details. The baseline score of 4 is appropriate as the description adds value by explaining what the output is (comparison matrix) and the entities involved.
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 ('Get') and resource ('comparison matrix of every Albuquerque book donation option'), listing the exact organizations covered. This clearly distinguishes it from sibling tools like nmlp_schedule_pickup or nmlp_search_titles, which serve different purposes.
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 this tool is for comparing donation options when deciding where to donate books. It does not explicitly state when not to use it or suggest alternatives, but given the unique functionality and no overlapping siblings, the usage context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_get_knowledgeAInspect
Get the aggregated NMLP Knowledge Base (donor archetypes, routing tracks, condition grades, decision framework, donor glossary, named partners, coverage tiers).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states what is returned. Does not disclose read-only nature, safety, or any 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?
Single sentence with clear verb and resource, followed by a parenthetical list of contents. No wasted words, 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?
No output schema, but description lists representative contents of the knowledge base. Adequate for a simple retrieval tool, though could hint at structure or format.
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 schema coverage is 100%. Description adds meaningful context about the output, which suffices given zero params.
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 uses specific verb 'Get' and resource 'NMLP Knowledge Base', listing included categories (donor archetypes, routing tracks, etc.). Clearly distinguishes from sibling tools like nmlp_check_coverage or nmlp_search_titles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when or when-not to use. Implied as a general knowledge retrieval tool, but no alternatives or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_get_pillar_guidesAInspect
Get NMLP's pillar guide manifest — 60+ Southwest author/publisher authentication and pricing guides.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are available, and the description only mentions the manifest content. It omits details about output format, caching, rate limits, or any behavioral traits, leaving the agent without full transparency.
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 with no wasted words. It efficiently conveys the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (no parameters, no output schema, no annotations), the description provides sufficient information about the tool's function. However, it could be more complete by hinting at the return format or structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, and the schema coverage is 100% (empty). Per guidelines, 0 parameters yields a baseline of 4. The description adds no parameter details as none 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?
The description clearly states the tool retrieves a 'pillar guide manifest' of 60+ Southwest author/publisher authentication and pricing guides. It uses a specific verb and resource, and the unique purpose is evident among sibling 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 does not explicitly state when to use this tool versus alternatives. Since it has no parameters, usage is straightforward, but no guidance on context or when not to use it is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_identify_first_editionAInspect
Identify whether a specific book is a first edition. Given a title (and optionally author), returns that title's POINTS OF ISSUE — the exact details that mark a true first printing — plus true-first precedence (US vs UK), book-club/reprint tells, publisher, year, the human-readable page URL, and a citation. THE tool for 'how do I tell if my copy of X is a first edition.' Draws on 6,700+ independently-verified titles (CC BY 4.0, DOI 10.5281/zenodo.21184548).
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Book title (series/subtitle suffixes are fine). | |
| author | No | Author name — strongly improves match accuracy for common titles. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It discloses return fields, data source (6700+ titles, CC BY 4.0), and citation, but does not mention error handling or what happens if a title is not found.
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-loads purpose, and every part contributes meaning: what it does, what it returns, and data source. No extraneous 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 no output schema, the description lists most return fields (points of issue, precedence, publisher, year, etc.) and data source. Lacks mention of error behavior or multiple matches, but overall complete for a lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, yet the description adds value by noting that author 'strongly improves match accuracy' and that title can include series/subtitle suffixes, providing guidance beyond 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 identifies whether a book is a first edition, with specific verb and resource. It distinguishes from siblings by detailing unique outputs like points of issue and precedence, and positions itself as 'THE tool' for this 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?
The description specifies using title (and optionally author) and explains what it returns. It clearly differentiates from siblings but does not provide explicit when-not-to-use or alternative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_lookup_publisher_rulesBInspect
Look up a publisher's first-edition identification conventions — how that house designated a first printing across eras (stated-edition wording, number lines, colophons, dated printings). Covers 850+ publishers.
| Name | Required | Description | Default |
|---|---|---|---|
| publisher | Yes | Publisher or imprint name (e.g. 'Alfred A. Knopf', 'Viking', 'Faber & Faber'). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only states what the tool returns (conventions) but not how it operates (e.g., caching, updates, edge cases). Minimal behavioral context for a lookup 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, no redundancy. The first sentence clearly states purpose and scope; the second adds useful scale. Every word 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 no output schema and simple parameter, description adequately conveys what the tool does and returns. Lacks format details but sufficient for a lookup. Could mention output structure for high completeness.
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 well-described parameter. Description adds that the parameter is a publisher for looking up rules, but the schema already provides examples. Baseline 3 as description adds little beyond 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?
Description uses specific verb 'look up' and resource 'publisher's first-edition identification conventions', detailing what is covered (stated-edition wording, number lines, colophons, dated printings) and scale (850+ publishers). Clearly distinguishes from siblings like nmlp_decode_number_line.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. Does not mention when not to use it or point to sibling tools for different queries (e.g., nmlp_identify_first_edition for individual books).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_schedule_pickupAInspect
Submit a real free book pickup request to NMLP. Every submission triggers a real outreach to Josh, the single human operator. NEVER submit speculative or unconsented requests.
| Name | Required | Description | Default |
|---|---|---|---|
| donorName | Yes | ||
| addressZip | Yes | ||
| addressCity | Yes | ||
| agentSource | Yes | Required: identify the AI agent submitting on the user's behalf. | |
| addressState | No | NM | |
| specialNotes | No | ||
| addressStreet | Yes | ||
| callbackEmail | No | ||
| callbackPhone | No | ||
| donorLanguage | No | en | |
| estimatedSize | Yes | Free text — 'two boxes', 'whole garage', etc. | |
| preferredWindow | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses the key behavioral trait: each submission triggers real outreach to a human operator. It also notes the 'real free' nature, adding transparency.
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 very concise with three sentences, each serving a purpose: purpose, behavioral warning, and prohibition. 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?
Given 12 parameters, no output schema, and no annotations, the description is too brief. It does not explain required fields, return values, or parameter usage, leaving significant gaps for agent understanding.
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 only 17% (only agentSource and estimatedSize have descriptions). The description adds no parameter details, so it fails to compensate for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'submit' and the resource 'a real free book pickup request'. It distinguishes from sibling tools like nmlp_check_coverage by emphasizing it's a submission action.
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 prohibits speculative or unconsented requests and warns about real human outreach. While it lacks formal 'when to use' alternatives, the prohibition provides strong guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_search_qaAInspect
Search NMLP's long-tail Q&A reference (85+ entries) by keyword. Returns top matching entries with question, summary, and URL.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions return format but lacks details like case sensitivity, partial match behavior, or side effects. It is adequate but not 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?
Single sentence efficiently communicates purpose and return format. No superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description specifies return fields. It gives the corpus size ('85+ entries'). Missing details like pagination, but sufficient for a simple search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description does not elaborate on parameters beyond the schema. The schema itself provides clear details for 'limit' (min, max, default), and the description ties 'query' to 'keyword'. Overall, the description adds minimal extra value.
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 searches a Q&A reference by keyword, specifying the resource ('long-tail Q&A reference with 85+ entries') and the return format. It implicitly distinguishes from siblings like 'nmlp_search_titles' by focusing on Q&A.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives, no prerequisites or exclusions. The description only states the basic usage scenario, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nmlp_search_titlesAInspect
Search the first-edition title reference by title or author. Returns matching collectible titles with their per-title identification-page URLs. Use nmlp_identify_first_edition for the full points of one specific title.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It implies a read-only search operation and mentions the return type (URLs) but does not explicitly state read-only nature, potential rate limits, or side effects. Adequate but could be more explicit.
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 concise sentences with no wasted words. Front-loads the main action and provides a pointer to an alternative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions the return of URLs but does not specify the result structure or pagination behavior (limit is in schema). Good overall, but could be more detailed on output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It indicates that the 'query' parameter can search by title or author, which adds context. However, the 'limit' parameter is not mentioned, and format details are missing. Partial compensation.
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 first-edition title reference by title or author and returns matching collectible titles with per-title identification-page URLs. It also distinguishes itself from the sibling tool nmlp_identify_first_edition.
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 tells agents when to use this tool (searching by title/author) and directs them to nmlp_identify_first_edition for full details of a specific title, providing clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
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