District-level open data for India — Census, NFHS-5, NCRB, RBI and more, joined onto one geography.
Use to end an answer with a link the user can open — after any ranking, comparison or district lookup, and whenever they ask to see, map or visualise something. Returns a spatialindia.com/app URL showing exactly those indicators. One indicator draws a choropleth, two draw a bivariate map; a third cannot be expressed in a link and is reported back as dropped.
| Name | Required | Description | Default |
|---|---|---|---|
| view | No | Open the scatter plot or the ranking panel alongside the map. | |
| state | No | A state name to zoom the map to. | |
| districts | No | District names or objectids to pin. One district also opens its profile. | |
| indicators | Yes | One or two indicator ids. A third is dropped. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as readOnly and not open-world, but the description adds meaningful behavior: one indicator draws a choropleth, two draw a bivariate map, and a third is dropped and reported back. This clarifies output behavior beyond the annotations.
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 compact yet information-dense. It front-loads the primary use ('Use to end an answer...'), then covers behavioral specifics without wasted words. Every sentence 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 the simple tool shape, the 100% schema coverage for the parameters, and the readOnly annotation, the description adequately covers return behavior and edge cases (the third indicator being droy). No output schema exists, but the description explains the return value as a map link, so nothing critical is missing.
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 the baseline is 3. The description adds parameter-specific meaning by explaining the consequence of one vs two vs three indicators, which directly clarifies how the 'indicators' parameter is interpreted and what the returned link will show.
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 states a specific verb ('end an answer with a link'), a clear resource (a spatialindia.com/app URL), and the exact scope of use. It is clearly distinguishable from the sibling data-retrieval tools because it is about generating a link, not retrieving or analyzing data.
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 gives explicit timing: use it 'after any ranking, comparison or district lookup, and whenever they ask to see, map or visualise something.' This strongly indicates when the tool should be invoked, though it does not explicitly mention alternative 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.
Use when the question names two or more places and asks how they differ — "compare Pune and Nagpur on literacy and bank credit". One call, not one get_district per district. Write an ambiguous name as "Bilaspur, Chhattisgarh".
| Name | Required | Description | Default |
|---|---|---|---|
| districts | Yes | District names, "Name, State" where a name repeats, or objectids. | |
| indicators | Yes | Indicator ids from find_indicator. At most 5. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, covering safety. The description adds behavioral context beyond annotations by emphasizing the batch behavior ('One call, not one get_district per district') and by advising how to disambiguate names. It does not describe return values, but the read-only annotation lowers the burden.
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?
Three sentences, all relevant, with the most important usage rule front-loaded. There is no wasted wording or repetition of schema details.
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 read-only comparison tool with fully documented parameters, the description covers when to use it, how it differs from the obvious alternative, and naming nuance. It does not describe the response shape, but no output schema exists and the sibling find_indicator covers indicator selection.
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 the schema already documents both parameters and the disambiguation format. The description reinforces this with an example and the 'Bilaspur, Chhattisgarh' guidance, but it does not add substantial meaning 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 states a specific verb ('compare'), resource (districts), and trigger condition ('question names two or more places and asks how they differ'). It also explicitly differentiates from get_district, so an agent can select it without ambiguity.
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 an explicit when-to-use rule with a concrete example and clearly says 'One call, not one get_district per district,' which both provides the usage context and names the alternative. This leaves little to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use FIRST, before any other tool, whenever a question names a subject rather than an indicator id — "stunting", "land inequality", "bank credit". Returns matching indicator ids with their unit, source, vintage, caveat and district coverage. Every other tool needs an id from here; ids are not guessable and inventing one fails.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Plain words describing the subject. Every word must appear somewhere in the indicator — prefer two or three words ("child stunting", "land inequality") over a sentence. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only behavior, and the description adds useful behavioral detail: it returns matching ids plus unit, source, vintage, caveat, and district coverage, and warns that non-existent ids fail. This goes beyond the structured annotations without contradicting them.
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 sentences, front-loads the most critical instruction ('Use FIRST, before any other tool'), and includes concrete examples. Every sentence earns its place: when to use, what it returns, and why this step is mandatory.
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 lookup tool with no output schema, the description is complete: it explains the entry-point role, the input nature, the returned fields, and the failure mode. An agent has enough context to invoke it correctly without additional documentation.
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%, with the query parameter already well-documented as plain words and guidance to prefer two or three words. The description reinforces the subject-based nature of the query but does not add substantial new parameter meaning beyond the schema, so the 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?
The description names a specific verb ('find') and resource ('indicator'), and makes the scope unmistakable: it resolves a subject phrase to indicator ids. It also distinguishes itself from all sibling tools by stating that every other tool needs an id from here, so an agent can confidently choose it as the entry point.
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 gives explicit when-to-use guidance: 'Use FIRST, before any other tool, whenever a question names a subject rather than an indicator id.' It also says ids are not guessable and inventing one fails, which effectively tells the agent not to skip this step and to avoid fabricating ids.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use when the question is about one named place — "tell me about Bilaspur", "what is Pune's literacy rate". Resolves an Indian district name, including common alternative spellings, to its objectid and returns its values. Four district names are shared by two states, so if the name is ambiguous this returns every match with its state and no data: ask the user which one, then call again with state.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | District name, an alternative spelling, or an objectid. | |
| state | No | State name. Supply this only to disambiguate a repeated district name. | |
| indicators | No | Indicator ids from find_indicator. Omit to get the district's full profile — every published indicator in one call, which is usually what you want. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true and openWorldHint=false, the description adds valuable behavioral context: it resolves alternative spellings, returns multiple matches with state when ambiguous, and returns no data for ambiguous matches. The instruction to ask the user and call again with `state` is a meaningful disclosure beyond the annotations.
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?
Three sentences, front-loaded with usage conditions and examples, with no wasted words. Every sentence serves a purpose: when to use, what it resolves, and how to handle ambiguity.
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 lookup tool with readOnly annotations and full schema coverage, the description covers the key behaviors: return values, ambiguity handling, alternative spellings, and the user-follow-up step. There is no output schema, so the description's mention of returned values and the ambiguous no-data case is sufficient.
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 parameter docs already carry semantic weight for `name`, `state`, and `indicators`. The description reinforces the state-disambiguation flow but adds little new parameter meaning beyond what the schema already declares.
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 states a specific verb and resource: resolving an Indian district name to its objectid and returning its values. It contrasts clearly with siblings by anchoring usage to 'one named place', which separates it from compare_districts and rank_districts.
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 an explicit 'Use when' condition with concrete example questions ('tell me about Bilaspur', 'what is Pune's literacy rate'), and explains the disambiguation workflow. It does not explicitly name the sibling alternatives to use instead, but the single-place framing is clear enough to route away from compare and rank tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use whenever the answer will state a number to a user, and always when asked how a figure was produced, how old it is, or whether it can be trusted. Returns the source, vintage, definition, caveat, estimation method, licence attribution and the citation string for one indicator, or for the dataset as a whole when no indicator is given. Do not describe a number as measured or estimated without checking here — each district carries its own estimated flag.
| Name | Required | Description | Default |
|---|---|---|---|
| indicator | No | An indicator id. Omit for the dataset-wide vintage, sources and citation. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already signal read-only safety, and the description adds valuable detail: it lists the return fields, explains the default dataset-wide behavior when no indicator is given, and warns about the per-district `estimated` flag. This goes well beyond the structured annotations.
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 sentences, front-loaded with the most important usage rule, followed by a compact list of outputs and a crucial caveat. Every sentence earns its place with no fluff.
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 read-only tool with one optional parameter and no output schema, the description covers what it returns, when to call it, and the critical `estimated` flag nuance. An agent has enough context 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 fully documents the single `indicator` parameter, including the omit-for-dataset-wide behavior. The description reinforces this but does not add meaning beyond the schema, 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 states a clear verb-resource pair: retrieving methodology and citation data. It specifies exactly what the tool returns (source, vintage, definition, caveat, estimation method, licence attribution, citation string) and differentiates the one-indicator vs dataset-wide scopes, making it distinct from the sibling data-access 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 gives explicit trigger conditions: use whenever a number will be stated, or when asked about how a figure was produced, its age, or trustworthiness. It does not name specific sibling alternatives or exclusions, but the usage context is strong enough that an agent can decide when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Use for best/worst/highest/lowest questions over many districts — "worst 20 districts for child stunting in Uttar Pradesh". Check the indicator's polarity in the result before calling a tail "worst": for a higher-worse indicator the worst districts are order: "desc", for higher-better they are order: "asc". Districts the source never covered sort last in both directions and are never presented as an extreme.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many districts to return. Defaults to 20. | |
| order | No | desc = highest value first. Defaults to desc. | |
| scope | No | A state name to rank within. Omit to rank every district of India. | |
| indicator | Yes | One indicator id from find_indicator. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only safety profile, so the description adds value by disclosing the polarity-checking behavior and the handling of districts the source never covered: they sort last and are never presented as an extreme. This is meaningful behavioral context beyond the annotations and helps the agent interpret results correctly.
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 compact and front-loaded with the main use case, then follows with the critical polarity caveat and the missing-data behavior. Every sentence contributes to correct tool invocation without padding or repetition.
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 ranking tool with no output schema, the description adequately covers when to use it, how to choose the order parameter, and how missing districts are handled. The parameter guidance combined with the fully documented input schema gives an agent everything needed 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 schema already documents all parameters at 100% coverage, so the baseline is 3. The description adds valuable semantics by explaining how the 'order' parameter should be derived from the indicator's polarity, and clarifies the behavior for uncovered districts. This goes beyond the bare schema definitions.
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 states a specific verb ('rank'), a clear resource ('districts'), and the exact question types it serves ('best/worst/highest/lowest questions over many districts'). The example 'worst 20 districts for child stunting in Uttar Pradesh' removes any ambiguity, and the 'many districts' framing helps distinguish it from sibling tools like compare_districts and get_district.
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 clear usage context: use for best/worst/highest/lowest questions across many districts, with a concrete example. It does not explicitly name alternatives or say when not to use this tool, but the scope phrasing and sibling names make the appropriate choice reasonably clear.
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.
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County and tract-level health outcomes, behaviors, and preventive services from CDC PLACES
Normalized official data with provenance, aggregations, insights, free samples and agent access.
Normalized official data with provenance, aggregations, insights, free samples and agent access.
K-12 school district data for all 50 US states. Enrollment, demographics, and academics.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct role: finding indicators, resolving a single district, comparing two districts, ranking many districts, retrieving methodology, and building a map link. Compare and rank both handle multiple districts but descriptions sharply separate pairwise comparison from best/worst ranking, so an agent should not misselect.
All tool names follow the same verb_noun pattern in lowercase snake_case: build_map_link, compare_districts, find_indicator, get_district, get_methodology, rank_districts. The verbs are distinct and each noun is the tool's primary object, making the surface predictable.
Six tools is a well-scoped size for a read-only spatial indicator server. Every tool earns its place in a coherent pipeline of discovery, lookup, comparison, ranking, metadata verification, and map-link generation, with no redundant tools.
The core read-only workflows are covered: find an indicator, get a district, compare districts, rank districts, explain methodology, and link to a map. Minor gaps exist, such as no direct way to simply list all indicators in the catalogue or enumerate districts in a state without framing it as a ranking, but agents can work around these.
