Turn analyzed data into an Excel-like spreadsheet at a shareable URL: formulas, styles, filters.
Returns the full schema fragment for one advanced feature. The argument must be an exact name from get_schema().featureNames — it is a fixed enumeration, not a free-text description of what you want to build.
| Name | Required | Description | Default |
|---|---|---|---|
| feature | Yes | Feature name from get_schema().featureNames |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral disclosure burden. It clearly indicates a read-only retrieval ('Returns') and emphasizes the critical constraint of using exact enumeration values, but does not discuss error behavior or response format. Adequate for a simple 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 deliver the essential information with zero fluff: what is returned and how to determine a valid argument. Front-loaded action and resource make it easy to scan.
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 one-parameter read-only tool with a constrained enum, the description plus schema is fully sufficient. It references the upstream get_schema tool for obtaining valid names, making invocation unambiguous. No output schema is needed for a schema-fragment lookup.
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 sole parameter has full schema coverage with an enum and description. The description adds valuable semantic layer—'must be an exact name from get_schema().featureNames' and 'fixed enumeration, not free-text'—which goes beyond the schema description and prevents misuse.
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 starts with specific verb 'Returns' and object 'the full schema fragment for one advanced feature', clearly distinguishing from siblings like get_schema (which lists feature names) and get_sheet/publish_sheet. Purpose is unambiguous and well-scoped.
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?
Description explicitly instructs that the argument must be an exact name from get_schema().featureNames and is a fixed enumeration, not a free-text description. This gives clear context for when to use the tool, though it does not explicitly name alternatives 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.
Returns the base ReoGridJsonDocument schema and an index of advanced features. The base schema already covers cell values, formulas, styling, borders and merges — those need no follow-up call. Call get_feature_spec(feature) only for a name listed in the returned features[] array, passing features[].name verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the return structure (base schema plus features[] array) and notes that the base schema already covers certain aspects. It implies a read-only operation, and while it doesn't discuss errors or side effects, the tool's simplicity (no parameters) makes the behavior sufficiently transparent.
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 redundancy. The first sentence states the primary purpose; the second adds usage guidance. All information is essential 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?
Given the tool has no parameters and no output schema, the description adequately explains what is returned and how to proceed with the returned data. It mentions the features[] array and instructs on calling get_feature_spec. This is complete for the agent's needs.
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 trivially covers 100%. The description adds no parameter info because none exist, but it does clarify the structure of the returned features array, which is relevant to how the tool is used.
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 what the tool does: 'Returns the base ReoGridJsonDocument schema and an index of advanced features.' It specifies the resource (schema and feature index) and differentiates from the sibling tool get_feature_spec by explicitly limiting that tool's usage.
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 provides explicit when-to-use guidance: the base schema covers cells, formulas, styling, borders, merges, so no follow-up needed. It also directs calling get_feature_spec only for names in the features[] array, passing the name verbatim, which effectively states when not to use that alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetches a previously published sheet by its short ID.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Short ID returned by publish_sheet |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. 'Fetches' clearly indicates a read operation, and 'previously published' sets a precondition, but it does not describe error behavior, idempotency, or what happens if the ID is invalid. Minimal but not misleading.
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, well-structured sentence that starts with the main verb and includes all essential information without any redundant words. It is appropriately sized for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema), and the description gives the core purpose and precondition. However, since there is no output schema, it would benefit from stating what the return value contains. The sibling context (publish_sheet) helps, but the description alone is somewhat sparse.
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 schema description ('Short ID returned by publish_sheet') fully defines the parameter. The tool description adds no new semantic information beyond what the schema already provides, matching the baseline for high 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 uses a specific verb ('Fetches') and resource ('previously published sheet'), clearly distinguishing it from siblings like get_schema and get_feature_spec. The phrase 'by its short ID' also identifies the key input, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (must be a previously published sheet, so presumably publish_sheet should be called first) but does not explicitly state when to use this tool vs alternatives or provide exclusions. It leaves the timing and prerequisites to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Publishes a ReoGridJsonDocument and returns a public URL. Anonymous; rate-limited per IP. No expiry in Phase 1. The doc must conform to the schema returned by get_schema().
| Name | Required | Description | Default |
|---|---|---|---|
| doc | Yes | A ReoGridJsonDocument (format=reogrid-json, version=1) | |
| title | No | Optional human-readable title |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively covers key behavioral traits: authentication (anonymous), rate limiting (per IP), expiration (no expiry in Phase 1), and validation constraint (must conform to get_schema). This is substantial transparency beyond the bare action.
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 three concise sentences. The first sentence states the core purpose, the second adds key behavioral constraints, and the third adds a validation prerequisite. No wasted words; every sentence contributes necessary 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 that there is no output schema and no annotations, the description covers the main aspects: what the tool does, the validation requirement, and important behavioral constraints (rate limit, no expiry). It could mention error handling or the exact structure of the returned URL, but the essentials are present. For a simple publish operation, this is adequately 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 adds meaning by specifying that the 'doc' parameter must conform to the schema returned by get_schema(), which gives validation context not present in the schema property description. The 'title' parameter is already well-described in 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?
Clearly states the action ('Publishes a ReoGridJsonDocument') and the result ('returns a public URL'). This is a specific verb+resource description that distinguishes the tool from its sibling getters (get_feature_spec, get_schema, get_sheet).
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 contextual usage guidance: anonymous access, rate-limiting per IP, and no expiry in Phase 1. It also implies a prerequisite by stating the document must conform to get_schema(), which directs the agent to call a sibling first. No explicit alternatives or exclusions are given, but the context is sufficient for basic use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_feature_spec2 fields changedInput schema / properties / feature / descriptionPrevious value: -"Feature name from get_schema().features[].name"New value: +"Feature name from get_schema().featureNames"Input schema / properties / feature / enumAdded value: +[
+ "conditionalFormats",
+ "cellTypes",
+ "filter",
+ "outlines",
+ "protection",
+ "alternateRows",
+ "view",
+ "frozen",
+ "numberFormats",
+ "richText"
+]get_feature_specget_schemaget_sheetpublish_sheetClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Complete one verification method:
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Each tool has a clearly distinct role: get_schema and get_feature_spec are layered (overview vs detail), while get_sheet and publish_sheet are inverse operations. No two tools overlap in purpose or could be confused.
All tool names follow a consistent verb_noun pattern (get_schema, get_feature_spec, get_sheet, publish_sheet) using snake_case, making the action and resource obvious.
Four tools is well-scoped for a service that handles schema discovery, publishing, and retrieval. Each tool serves a necessary function without redundancy.
The tool set covers the core lifecycle of publishing (publish_sheet) and fetching (get_sheet), plus schema discovery (get_schema) and advanced feature details (get_feature_spec). No critical gaps are apparent for the stated Phase 1 scope.