Multiple backend changes to make use of the new mcp tools and improve…

[miss-o-soup]

Backend, MCP Integration, Skills & Data Access
Updated Fallbacks for Data Facets (src/server/steps/observations.ts):

• Dataset Source: Changed fallback from 'Data Commons' to an empty string '' (meta?.source || data.importName || ''). When neither metadata nor API import names exist, it now cleanly defaults to empty instead of injecting generic filler.
• Units: Changed default fallback for un-unitized variables from 'Dimensionless' to 'Unit not specified' (data.unit || 'Unit not specified').

Integration of Updated Data Commons MCP Tools & Remote Playbooks:

• Metadata Resolution (observations.ts): Concurrently query get_variable_metadata via MCP during time-series fetching to map exact dataset provenance names (isPartOf, source, url).
• Remote MCP Skill Playbooks (src/server/clients/mcp.ts & src/server/steps/data_discovery.ts): Implemented readMcpResource via JSON-RPC resources/read to dynamically fetch and inject remote playbooks (skill://data-commons-researcher/SKILL.md) into Gemini's system instruction.

Data Discovery Skill Rules & Lead-in Prompts (data_discovery.md):

• Rule 0 (Strict Verbatim Copying): Mandated copying variable DCIDs and names verbatim from MCP search outputs, prohibiting inferred/invented demographic labels and enforcing places_with_data verification.
• Rule 0.1 (Lead-in Intro Rules): Constrained the table card introduction to a 1-sentence framing description (12–22 words) explicitly attributing data to "Data Commons", following John Saito tone guidelines, and utilizing dynamic variation patterns (Patterns A–D).

Data Comparison Skill Prompt (data_comparison.md):

• Updated comparative insights schema instructions to produce clean, standalone plain-text comparative sentences across places.

BEFORE - The true facet information was missing defaulting to say Data Commons

AFTER - Now the true facet info from the metadata mcp tool is being used and displayed

[gemini-code-assist]

Code Review

This pull request introduces MCP skill playbook fetching and caching, refines LLM prompt instructions and schemas across several markdown files, and integrates concurrent metadata retrieval from MCP in fetchTimeSeries to enrich observation facets. The review feedback highlights critical improvements: caching negative results in fetchMcpSkillPlaybook to avoid repeated timeout delays, wrapping metadata parsing in a try-catch block to prevent parsing errors from breaking successful observation fetches, and correcting minor typographical errors in the query analyzer prompt.